MLOps Capabilities, Outcomes and Opportunities for Enterprise AI

Machine Learning Operations (MLOps) has come to be an important push for enterprises in 2021 and beyond – and there are clear reasons why this paradigm shift in Enterprise AI is upon us. Most enterprises who have begun data science and machine learning programs over the last several years have had difficulties putting even their promising machine learning models and proof of concept exercises into action, by deploying them meaningfully in production environments. I use the term “meaningfully” here, because the nuances around deployment make all the difference and form the soul of the subject matter around MLOps. In this post, I wish to discuss what ails enterprise AI today, sources of the gaps between production and proof-of-concept, expectations from MLOps implementations and the current state of the discourse on MLOps.

Note and Acknowledgement: I have also discussed several ideas and patterns I've seen from experiences I've had in the industry, not necessarily in one company or job, but going back all the way to projects and programs I've been in over the last seven to ten years. I don't mention clients or employers here as a matter of principle, but I would like to acknowledge mentors and clients for their time and energy and occasionally their guidance as well, in the synthesis of some of these ideas. It is a more boundaryless world than before, and great conversations are to be had regardless of one's location. I find a lot of the content and conversations regarding data science on Twitter and LinkedIn quite illuminating - and together with work and clients, the twain have constituted a great environment in which to discuss and develop ideas. 

What ails Enterprise AI today?

Surely, with the large scale data pipelines companies have access to, the low cost of cloud native solutions, and the high level frameworks for building machine learning models, things should have become easier? Enterprises still seem to be failing in their efforts to build AI programs for many reasons despite these upsides. For one thing, building models has become easier than before. It takes less time to take (good enough or clean enough) data and build prototype models with this data. Regardless of how many hypotheses you have as a business leader or data scientist, you’re more likely in 2021 to be able to collect data and build prototype models with this data, than you were able to in previous years. In the past, you may have had to go through several organizational hoops to get your data, and then prepare this data and then build models. All of these processes have become a bit simpler in 2021, thanks to enterprise data stores maturing, frameworks for building ML models become better known, and greater numbers of data scientists being available to build models. While things are still quite complex for the uninitiated, those on the growth curve in data science have found this phase to be adding productivity to their prototyping efforts.

What hasn’t changed, though, is the process of taking these models to production. The model is largely seen as a software asset, and productionization of the model has been seen in this limited context. As we will discuss, it is important to challenge this mindset if we’re to build effective machine learning systems for production. The gap, therefore, between proof-of-concept models like we’ve discussed above, and production scale implementations of such models, is large. Real world implementations are more complex and tedious, and often, the hypotheses we want to build models for are a bit more well defined – this necessitates extensive data processing, profiling and monitoring. But the complexity doesn’t end there, even though there has been an effort on the part of MLOps practitioners to build end-to-end pipelines. You’ll note that none of these are ground-breaking realizations. MLOps is a practical field, thus far, intended to make all these models work for enterprises – but as we will see below, the practical nature of this field encompasses a number of domain, statistical, cultural, architectural and other considerations.

I wish to suggest before diving deeper into this post, that this trend towards MLOps adoption represents a noteworthy change in how enterprises see ML system architecture in 2021, as opposed to the previous decade. In a manner of thinking, represents a move towards the “plateau of productivity” in enterprise machine learning.

Considerations for Enterprise AI – from MLOps, Data Science and Data Engineering

Domain Considerations Matter in Data Science, Data Engineering and MLOps

I wrote several years ago on this blog that domain knowledge is an important element in doing data science. Back then, as a data science neophyte learning from early experience in pure data science roles, I had made several observations about the impact of domain understanding on how quickly we can arrive at hypotheses for formulating data/AI problems. Looking back, this was an important lesson, because I now acknowledge the importance of domain knowledge every time I work on a data science project, or each time that I enable a data science team to be successful. Whether this is my own domain knowledge or that of SMEs, I am grateful for it, because without it, we could build anything, and it wouldn’t ultimately matter to anyone. Domain knowledge gives purpose to data and AI efforts. Without speaking to the domain experts and SMEs in various projects (finance, manufacturing, retail, energy and other industries), there would be little to no chance of timely and cost-effective success in characterising, ideating about and solving these problems.

It may not be immediately evident, however, that domain considerations matter in MLOps (and DataOps). Without an understanding of data generating processes, data formats, sources, rates, types, and data organization patterns, data fields, tables and even some of the process characteristics, we cannot understand data generation or transformation processes in enterprise data pipelines. We can also not understand how models are to be implemented, and what deployment means in different enterprise or customer contexts. When building and architecting machine learning systems, we end up needing to discover these details if we haven’t already. MLOps therefore cannot be ideated about in a vacuum, without consideration to the domain of the problem, or without consideration to the unique challenges of deploying models that domain. MLOps in logistics and supply chain problems, therefore, will be quite different from MLOps in manufacturing, retail or banking domains.

For instance, if we were building a classification model to sort defective parts from good ones on a manufacturing shop floor, we may need a real-time deployment system, with consideration to latency, edge based deployments of models, opportunities to inspect models as downstream processes or metrics may indicate process failure modes, and so forth. These considerations may not exist if we were building a system for enhancing ad revenue in a platform software company. The considerations there around uplift from pushing ads to new customers may require edge based deployments of a different kind, or federated learning needs, that may be unnecessary in the manufacturing example we discussed. To use an analogy, deployments are like different flavours of ice-cream, each requiring a different kind of appreciation. A failure to realize this may lead to difficulties in enterprises that may inadvertently underestimate the complexity of MLOps, of their own domain processes, or both.

Simplistic, Linear Pipelines Don’t Get Us Over the Line

The current thinking around MLOps is somewhat simplistic and linear, and I mean this in a specific way. There is a lot of discussion around data workflows and pipelines, metadata generation and management, and the metrics around model training and model performance. These are discussions around the management, transformation and profiling of data. Datasets are important to MLOps pipelines, and inasmuch as agility in data science is concerned, I’d even say that they are primary.

However, this notion of thinking only about the software and application-level implications of models and their deployment doesn’t address some of the needs from MLOps pipelines for enterprises. Notably, model interpretability and explainability, managing a diversity of deployment patterns (edge, batch, real-time or near-real-time), and the need to build repeatable pipelines or reproduce results. These problems cannot be broken down into just software applications, and require statistical rigour and attention to changing domain patterns. In fact, there is sometimes a desire on the part of ML engineering or MLOps practitioners to see these more statistical needs of MLOps as “not software engineering” and perhaps therefore “not easy to build for” – both of which may not be true, especially as the space of tools and implementations of statistical models for interpretability/explainability expands just as ML implementations have expanded.

Imagine that you have built an MLOps pipeline to build a dataset for a specific use case, and deployed it and the model eventually, and all’s well. If there’s a need for a new use case, you’re likely to begin back at square one, and build new pipelines, especially if you don’t have a clear and unified data model. As we will discuss in a later section on architecture, this is important to consider in ML engineering – more than one use case may require your data pipeline. This also means that simplistic and linear pipelines can only serve a limited purpose when you’re required to build many such pipelines across enterprise workloads.

For instance, it is possible to build SHAP scores for models given a specific dataset, and for companies with regulatory needs, there may be a reason to deeply analyze and publish results such as these. Therefore, MLOps shouldn’t only be about building simplistic DAGs or workflows in your YAML engineering tool of choice, or building and deploying metadata-tracked machine learning training/inference workflows. These are necessary, but insufficient for good MLOps implementations – chiefly because there are many other statistical and probabilistic considerations around MLOps which also deserve attention.

Data Architecture Before MLOps, but Business Needs First

There was an interesting discussion here recently around the theme of “Data before models, but problem formulation first”. The interesting article in question describes the specific challenges of thinking about data science problems based on business problems, and being “data-driven” in thinking about and building models for our hypotheses. I posit that a similar paradigm applies to MLOps. Data architecture understandably matters a great deal for success MLOps implementations, because it encompasses very foundational organizational processes and needs around data collection, storage and management, governance, security and quality, access patterns, ETL/ELT, sandboxes for analytics, connections to BI and reporting systems, and so on. Ultimately, this complex web of processes and technologies (because data architecture is more than just storing and retrieving data) is meant to perform some function of the business. As W. Edwards Deming said, “Data are not collected for museum purposes” – they are collected for a decision to be made, or for some end use. In the world of MLOps, we enable such decisions to take place on top of the data provided to us through an enterprise data architecture such as this one described above.

While typical enterprise data architectures are driven by the capabilities of tools and cloud scale applications more and more (because of the economies of scale of cloud providers, and the low barriers to entry), there is an important set of decisions every enterprise data architect has to answer for, around the specific needs of the organization, and how the architecture in question enables that to happen. Seemingly trivial decisions taken at the design phase of a data lake or data warehouse can have long lasting implications for the delivery of value from analytics, machine learning and MLOps. Data architecture is certainly important for MLOps, but the more fundamental needs of the organization – the kind of data required, the strategic importance of it, the decisions that need to be made across use cases, security and access patterns for data analysis and data science, and many more operational aspects of data – all of these are important and have a bearing on MLOps effectiveness too. So if you’re a data scientist or MLOps practitioner looking to improve your impact and effectiveness in solving problems, understand the underlying data architecture more deeply first. Sometimes, doing this can be hard – especially if there are no stakeholders who can explain it well – but this kind of fundamental understanding and context are highly underrated and have an outsize impact on the success of data science and ML programs eventually.

The Enterprise Model Sanctuary: Many Simpler Models, A Few Complex Models, and Other Combinations

A cursory glance at machine learning and MLOps forums, discussions and content indicate that the thinking around model development techniques is method centric, and not business centric. A large number of the discussions are a consequence of what’s required for companies at scale innovating on a few complex models with huge amounts of data – and these are legitimate and interesting discussions for sure. For example, most MLOps discussions I have come across seem to discuss the deployment of deep learning models. They discuss text and unstructured data processing, and complex image processing pipelines. Whether the use of tools like Kubeflow for training and deploying models in a distributed fashion, or the use of MLFlow for tracking metrics and performance, these are all legitimate considerations that may solve subsets of the ML deployment space. However, machine learning state-of-the-art is rarely required for enterprises looking to get value out of their specific use cases. The large majority of use cases in the industry are for simpler models, though and this is why simpler pipelines could do a large part of the value creation. I say this from experience and with confidence, having seen numerous projects where managers struggle to make sense of ML outcomes for their business, but have less difficulty making sense of data aggregations, summaries and statistics based on the data. The enterprise model ecosystem is more likely to resemble a zoo or even more accurately a sanctuary of different models, where each model may have its own specific needs and requirements.

Model development in mature organizations generally is an afterthought to carefully evaluating data and the evidential findings from it on merit, and then exploring hypotheses subsequently. Enterprises at lower levels of maturity have difficulty getting value from such an approach, however, and many leaders there may still rely on dashboards and reports. Clearly, there is an important and untapped market in business intelligence from big data. There is also a huge market for implementing simpler models based on clearly defined hypotheses. In many cases, enterprises may need many such simpler models, one for each stratified part of a specific use case. For instance, if you’re a market research firm estimating sales in a market segment, you may wish to build many such models for each sub-segment. If you are an equipment manufacturer doing quality checks using machine learning models, you may wish to use attribute based classification models, one for each product line, and perhaps you want to build many of them. The true value of MLOps in these cases is not in managing the complexity of deployment for one complex model, but in enabling many simpler models to be taken to production quickly and efficiently. These simpler models may then provide a baseline with which to build more complex models as needed.

Machine Learning Systems are Stochastic, Not Deterministic

Perhaps I’m stating the obvious, but it needs to be said. The underlying nature of data generating processes and machine learning models is stochastic and not deterministic. Whether we’re talking about manufacturing process metrics, banking and finance transaction data, energy sector data around load, power, usage, and so forth – all of these data are generated from stochastic data generating processes, even if they come from engineered systems. Machine learning models are also never exact mathematical formulations – they are almost always stochastic processes. There is a little to unpack here, so I’ll get into a few instances. What this stochasticity means, is that machine learning models exhibit variability in results from situation to situation, and that this will be quite evident in production. In order to begin building machine learning systems, we need to perform exploratory data analysis prior to training time, prepare features for our hypothesis, check assumptions based on the feature and the model formulation, and then build models and evaluate them. What it also means, is that we need to build safeguards to ensure that these assumptions are valid when doing production scale inference. It means that we may have to reformulate problems, as the underlying conditions of the data generating process changes. In case of deep learning models, sophisticated tensor transformations and training loops are required as part of the normal training loop of deep learning models.

When the model is eventually trained to the required level of performance and rendered, they too represent a solution at a specific point in time. MLOps is not about “train once, deploy everywhere”, but about “routine retraining and redeployment”. This makes ModelOps and the continuous training lifecycle of model development as important a consideration in MLOps as DataOps is. A lot of discussion around MLOps today is centered around data preparation – and the motivation for this, of course, is the fact that there are significant data preparation challenges that data scientists face. However, model training in the real world cannot be wished away by despite the prevalence of AutoML, although AutoML tools are one path for progress. As of 2021, for most use cases, model definition and training is still done manually, even if tuning and optimizing the model are automated. In MLOps lingo, we are referring to the importance of using feature stores, and their impact on data drift and concept drift analysis. While a healthy discussion is in progress on these topics, the instrumentation in actual implementations of data drift and concept drift identification and measurement tends to vary. Some tool chains are ready for this change, and others just aren’t.

More broadly, some MLOps implementations may account for these stochastic and probabilistic characteristics of ML systems, because their data scientists ask the hard questions after training and during/before deployment. On the other hand, it is likely that most MLOps implementations today treat models merely as pieces of software. The latter pattern leads to the unfolding of technical debt of various kinds later in the lifecycle of the system. This technical debt currently represents building additional regulatory checks, doing interpretability analysis, meta-data logging, model performance metrics, and so on – and over time, this set of secondary considerations may grow much bigger.

Changing Skillsets and Roles for MLOps

Companies looking to hire top ML talent as of 2021 are pushing for a greater number of high quality data engineers with MLOps skill sets. This is in contrast to emphasis on data science hiring in the past. Hiring pipelines for data and AI roles (I’ve seen a few different ones over the last few years) tend to emphasize programming, statistics, databases and specific technologies for data science – of late, this is largely SQL, Python, with a smattering of distributed frameworks and tools, and skill sets in deep learning, tabular data analysis and the associated frameworks and tools for solving problems in this space. For data engineering roles, over the years I’ve seen skill requirements specifying systems programming and strongly typed languages such as Java and Scala, experience working on JVM languages, in addition to SQL, databases, and a lot of the back-end software engineering skill sets we see for application developers elsewhere. For data engineers working on big data technologies, there’s very often a need to be familiar with NoSQL databases, or graph databases, depending on the role and use cases, in addition to the Hadoop-and-friends ecosystem, and cloud engineering skills such as AWS or Azure. While the data scientist’s role and skill set has come to include domain considerations, advanced statistical and ML models, cloud-native and large scale data science and deep learning and communication/presentation of data and insights, the data engineer’s role has become broader around systems engineering and design.

Someone said (in fact, in this talk) that data engineers ought to build frameworks, and not pipelines – and this is a fair assessment of how to use this broad and useful skill set in data engineering. There has been a healthy discussion in various forums, talks and the like on ML engineering roles which combine elements of these two different skill sets. All of these conversations around skill sets are important context for where we’re heading in data science and engineering space overall too. MLOps, unlike DevOps before it, should not be constrained by the limited value addition possible outside of data scientist or data engineering roles (the bulk of DevOps roles are administrative in nature). They cannot be construed as or see themselves as configuration file engineers, for lack of a better term. In fact, their role could be much broader – as systems engineers spanning a range of capabilities in both data science and data engineering, while not possessing expertise in any one of these (themselves diverse) areas. MLOps roles should perhaps also emphasize domain knowledge or expertise of some kind – since ultimately, the outcomes here are practical and related to business value from ML. There are many outcomes and opportunities for talent and skill sets for sure, but these stand out as being relevant. What is for sure is that the data scientist’s role has changed (as has the data engineer’s), and the old and unyielding challenges being faced by data scientists are taking on new definitions and manifestations – thereby requiring new mindsets, new skill sets, and new processes to come forth.

In my view this churn in the extant data science and engineering role paradigm is a welcome development because enterprises first want to realize value from DataOps and MLOps simultaneously today. As we will discuss later in this post, while models are important, business managers will continue to derive value from analytics and reports – and perhaps there has never been a better time to build on that need than 2021. Also, the emphasis on data engineering roles as on date is well-founded. From practical experience as a data scientist who worked on a range of problems from relatively simple ML to complex deep learning models, I will happily acknowledge that data engineers I have worked with were indispensable to the success of the projects I succeeded on. However, leaders hiring for ML roles should not think that the role of the data scientist is no longer required. I believe this emphasis on data engineering is a passing trend as enterprises build foundational pieces that enable value from data. The focus will therefore shift once again to business value from data, and that this automatically means that statistical, data science and ML skills will continue to be in vogue through this shift and afterwards.

Don’t Ignore Decision-Making Culture

Organizational culture matters a lot for the success of MLOps, as much as it does for any digital transformation program. MLOps represents, in a way, a desirable end-state or the happy marriage of data science and data engineering in a given enterprise and data architecture context. However, both data science and engineering can only be valuable and effective in organizations whose leaders think about and talk about data and use the data and insights from these data for taking decisions. The latter is a cultural synthesis, and not just a technology adoption process or workflow that one can execute on demand. Being a cultural matter, it has to do with behavioural and attitudinal patterns that ultimately enable data and insights to be used for decision making.

The adoption of data driven decision making represents a shift from thinking about business processes, systems and decisions in terms of rules (“Rules are for lazy managers”, to paraphrase Simon Sinek), to an open-minded thought process around data and AI systems. When leaders stop thinking in terms of rules, and start thinking in terms of systems, they are often imagining situations of change, synthesis, formation and deformation of patterns, structures and interactions. They begin to see their role as an influencer more and as a commander less, and this shift in thinking can enable them to make subtle changes to their managerial approach, driven by data.

In the earlier post I wrote about OODA, and the AI-enabled generalist, there is a point I make about the decision making language of organizations. This kind of development of a decision making language requires a way of thinking about the enterprise’s systems, processes, and also the ML models in new ways. It requires an openness of mind in decision making to adopt models as thinking tools. In a sense, the modern AI-empowered generalist could be seen as a prototype for a supreme pragmatist. Enterprises want rational actors at their helm, at least for the functions that require data driven decision making – and such rational actors can be groomed in a culture that doesn’t shy away from challenging the current rules and norms on decision making, and is willing to look at data and models.

Data/AI Exponents as SMEs and Future Leaders

Organizations come to embrace data, ML or even MLOps so that they can ultimately derive value from data, and this cannot be done without talent that unlocks value from data. Be this talent data science talent or data engineering/architecture talent, there is both a topical / functional need and a strategic value of these roles in enterprises, and this tends to be overlooked in data strategy. This is because of the value such individuals accumulate over time, as they build data pipelines and AI/ML models, accumulating a lot of knowledge about business processes, customers and also domain knowledge in the process. When you have a data scientist in your team who has built a few different models that explain different elements of your business, processes or customer behaviour, they become invaluable assets for both developing further models, and for analyzing customer or business or process behaviour. Such individuals can also become effective leaders and transition to process management roles.

MLOps and DataOps engineers in an organization can therefore themselves be considered Data/AI SME roles – and this is an important source of value that is often overlooked in organizations. A lot of organizations still see data/AI resources as just means to an end, but in fact, many of these roles can become storehouses of domain knowledge. MLOps can potentially enable the tacit knowledge from such individuals to be effectively captured for process management as well – this may be an important opportunity for value creation from MLOps. MLOps can also accelerate the development of data-driven leadership talent. When exposed to the models used to take decisions, and the specific mechanisms of taking such decisions, leadership potential for process leadership is improved.

In an earlier post, I discussed the importance of higher-level decision making languages, the OODA decision making loop, and how AI can enable a new generation of generalists. I would suggest that this is a useful idea to consider in the broader context of building a data-driven decision making culture.

“Data Before Models” also implies “Models After Data”

The purpose of this heading is to draw attention to the fact that the best data pipelines won’t help, if we aren’t doing much with the data we prepare. We have to eventually build models with this data of one or other kind for actually taking decisions. Many recent discussions around MLOps talk about data-centric AI, and above, we have discussed data architecture and other elements of enterprise systems and culture that contribute to MLOps success. We have also discussed the stochastic nature of data generating processes and machine learning systems. There are important implications from the core ModelOps processes as well, and we will discuss them here, finally. The process of developing models, as I have discussed above, has become easier now than ever before, at least in software. The careful formulation and evaluation of model hypotheses, statistical analysis of the input data and features, and the checking of assumptions – these still remain harder, more tedious and less trivial, as they were before. This necessitates the importance of statistical analysis and exploratory data analysis. Without these foundational steps, ML models can be built with high bias or high variance, thereby setting up the use case for higher failure rates and lower effectiveness overall. This bears introspection and repetition, since there seem to be two schools of machine learning and data science professionals – there seems to be a group of professionals who believe strongly that mathematical and statistical thinking are important for doing data science. There’s another group of professionals and practitioners who think otherwise, that the software elements of data science modeling can be learnt by someone without knowledge of statistics or machine learning.

In my experience, the statistical analysis and EDA are fundamentally important for machine learning – they forms an integral and important part of extracting value from the data we have, and making sense of it, before we solve problems. A number of business situations require us to think in terms of data distributions and stochastic processes. To build things that scale within MLOps pipelines, some of us may need to have an open mind about exploring the mathematical underpinnings of things like gradient descent or batch normalization, or activation functions. This open-mindedness is important for a key reason – a lot of MLOps engineers being trained today may assume that the data science is easy, or trivial, because people who don’t know statistics are building models, or because they can, if they just follow a simple workflow. I know this to be patently untrue – if you want to develop a model worth anything in an enterprise, you may have to start from formulating and thinking about the business problem, get to the EDA and statistical analysis and built out tests for assumptions checking, and then experiment with different models. You have to get into the probability and statistical analysis eventually, or you will be forced to rediscover the effectiveness of these mathematical and scientific methods. Even if you manage to build one or a few models, there will be situations where you’re required to explain these models. Not only will ML engineers or data science engineers be more confident when they are able to reason about the mathematics of machine learning, but their ability to build and scale systems for the enterprise improves. Their ability to think about the implications of these models for different related use cases, for different deployment modes, different source data, and different data quality considerations also improves. By checking assumptions on the features, they could stave off big challenges that may arise when the model is implemented in production.

Statistical analysis and machine learning model development have been core and will be core to data science, regardless of the peripheral engineering required for realization of value. Data engineering and MLOps as allied fields help realize this value at enterprise scale. It is the process of data science and model development that ultimately converts data into insights – and insights are the primary purpose of investing in enterprise data and AI projects and programs in the first place. They will therefore continue to be a good bet for practitioners in future – as long as they realize that those skills alone cannot take them over the finish line.

Concluding Remarks

I hope that you’ve benefited by reading this rather lengthy post on MLOps and Enterprise AI. If anything, it allowed me to explore my own experiences, document a few patterns I see in the development of truly enterprise ready AI, MLOps toolchains and capabilities, and also explore sources of value from MLOps for enterprises. If you have questions or ideas, please leave a comment or tweet to me at @aiexplorations.

Further Reading/Listening

  1. Data Science is Different Now, by Vicki Boykis:
  2. Problem Formulation Comes First by Brian Kent on Crosstab.io
  3. Build Frameworks, Not Pipelines – a Data Engineering Talk on PyData
  4. From Model-Centric to Data-Centric AI – a discussion on Enterprise scale AI with Andrew Ng and others
  5. ML Engineering for Production – another discussion on ML for production with Andrew Ng and others

Emphasizing the Basics: Structured Data Science Mentoring

Data science, machine learning and AI are constantly growing and burgeoning fields, with research that’s spilling over at the seams in terms of the sheer volume of it all. Every day, I receive numerous references to interesting papers on my Twitter feed, thanks to Arxiv daily and such accounts there. I also see papers explained with code, and references to ML products and systems in numerous contexts. This is all overwhelming beyond a point for a professional who doesn’t have a specific focus area. Speaking pragmatically, and from the tree of knowledge (which is always bound to be vast), it is a feature of every single human endeavour to exhibit this kind of complexity as we spend more and more time exploring things, farming ideas and understanding new possibilities in these areas.

Data scientists are going to be at different levels of competence and may be differently placed to take on challenges they are asked to face – the role of the mentor (regardless of the type) is to systematically challenge the data scientist to discover new innate potential and develop such potential to increase their overall capabilities and effectiveness.

The Data Science Mentoring Challenge

Mentoring can be a hard task for this reason – a lot of people (understandably) gravitate towards complex models that are meant for specific purposes, without fully understanding the details and the exact mechanisms behind simpler machine learning and statistical modeling methods. The problem with this is two fold – a) reliance on libraries and frameworks with implementations that already exist, and b) inability to characterize, apply and explain common and simpler techniques to actual real world problem statements. Part of the problem here is the sensationalization of research in the media. Open research without borders is important and pivotal for speedy progress in technology areas like ML. But we’re also seeing a lot of misinformation including sensationalization of advanced ML techniques and when some of gets parroted by professionals (some of who may become hiring managers) we see the problem proliferating into the world of work as well. I’ve interviewed my fair share of individuals who understand, say, an LSTM unit’s different gates but aren’t comfortable explaining autocorrelation techniques or ARMA models. This gap probably stems from gaps in mentoring and coaching, which ideally should emphasize basics first.

I’d posit that the role of the mentor has changed, in data science, over the last several years, and I would say it has changed most significantly in the last two years. In the future, data and AI mentoring will look different from what it looked like in the past five years. This is because the nature of the job of a data scientist (or alternatively an AI/ML engineer) has also changed. Despite developments in Automated Machine Learning, we’re inundated with situations in the real world, where we require human expertise to get through data science and machine learning problems. This human expertise manifests in three processes: problem characterisation, problem formulation, and problem solving. We need real, human data scientists (not just an AutoML tool) to look beyond the obvious automations such as hyperparameter or architectural searches, to reason about the nuts and bolts of problems, interpret the problem domain and reason about different kinds of hypotheses and how they make sense.

This makes the process of mentoring for data science different than it was, in certain specific ways. For one thing, mentors today create the field of problems or opportunities that will exist tomorrow. Data scientists today experience an overload of information as can be expected, from different sources. From Arxiv and Springer papers and articles, to new research and code, new books and new frameworks and algorithms, there are plenty of things to learn on a daily basis. However, the broader skill set of the data scientist even today can be characterized into four key areas: basic, business, functional and frontier skills.

Broad Characterization of Skills for Modern Data Science
  1. Technical skills: There’s the need for a strong foundation that enables general effectiveness in a data science role. This includes good skills across statistical analysis fundamentals, leading into the key principles that enable statistical learning models to be built, and a sound understanding of the mechanism behind common algorithms such as regression, tree algorithms, search and optimization methods
  2. Business skills: There is a strong need for data scientists who can reason about business processes and systems, and understand how data may be generated, how it may flow, and what insights may be required of it. Not only is this is a key skill to have fruitful interactions with clients and stakeholders, but it is also important to narrow down to the right level of depth for the job in terms of satisfaction and effectiveness.
  3. Functional skills: There’s the need for effectiveness on the job, at a functional level, which not only includes technical competence at the statistical, mathematical and code levels, but at the level of processes and good practices such as clean code, change management and reproducible research. One could also see more advanced machine learning and feature engineering techniques as being part of the functional skill set.
  4. Frontier skills: There’s research that’s expanding at multiple frontiers, which is hard for even experienced data scientists to keep up with, if they’re really interested in furthering their career beyond the obvious and evident challenges of day-to-day work.

Mentors: Different Levels

The role of mentorship has also become specialized in the last two years, which is, in my view, one of the changes most representative of the maturation of the field of data science. Mentors today can be at different levels of skill and still add value to different kinds of data science and analytics roles. For the sake of this discussion, I’d classify mentors today into two kinds – the “breadth mentor” and the “depth mentor”. While both kinds of mentors possess certain common skills, especially on the interpersonal communication front, they may have different approaches to technical, functional and research level mentoring.

The breadth mentor is an individual with plenty of experience in data science, perhaps in a consulting setting, that can provide generally correct advice to data scientists with the development of broad skill sets, ranging from basic statistical analysis, to advanced algorithms. The nature of the mentoring here is on developing a well-rounded data scientist, rather than an expert in a specific field.

The depth mentor by contrast, is someone who has deep experience in a specific area of industry or technology and has deep experience in bringing this field together with data science. Examples of this kind of data scientist would be an NLP researcher, or a researcher in the field of robotics, both of who may be expert practitioners of data science methods in their specific areas, but without the broader knowledge of consultative data science methods.

Depending on the needs of the business and the data scientists in question, the appropriate kind of mentor has to be chosen – and this shouldn’t be done lightly. For example, bringing a breadth mentor to an AI product firm may have some advantages, but if the firm is solving problems in a specific space, this may not work out so well. Similarly, bringing a depth mentor to a consulting firm can help grow a specific practice (or a new one) but may not benefit the broader data science efforts across different business domains there.

Structuring Mentorship in Data Science

Mentors (and hiring managers) in general should emphasize the importance of the basic skills listed above. In my view, when a data science candidate has the correct understanding of the essential basic statistical ideas and common algorithms, it becomes a lot easier for them to grasp more advanced ideas such as in deep learning, when this is required. Mentors can build better basic skills in data scientists by challenging their technical acumen.

Mentors should also emphasize business skills where relevant, and where the emphasis is on research, they should emphasize some of the frontier skills as well. Mentors in this context are expected to challenge the data scientist with relevant questions, and encourage a habit of systematically breaking down problems and asking the right questions. These business skills are important all the way up to solution architect roles and management, when crucial decisions have to be taken and hard questions will need to be asked often. Mentors can build better business skills in data scientists by challenging their problem understanding and characterisation.

Functional skills are important for effectiveness on the job. It is not okay for data scientists to theoretically understand a specific subject area, only to find themselves handicapped when asked to build a machine learning pipeline. Therefore functional skill mentoring is about challenging the data scientist on problem solving effectiveness.

Finally, frontier skills development depends on both the organizational or research context, and the data scientist’s interests. Mentors can provide helpful markers to enable the exploration of ideas, while emphasizing value from the research, and asking questions that keeps the data science researcher on track. The challenge the mentor can pose here is differentiated solution value and originality.

The Importance of Emphasizing the Basics

This brings me to the importance of emphasizing the basics. I see numerous individuals out there who are getting into data science and machine learning that are interested in getting right to the latest and greatest algorithms. For a while – and this has been a trend on LinkedIn and Twitter – budding data science aspirants post some of their work, where it involves the development of simple scripts or programs around computer vision, translation and such problem statements, thereby delivering an impression to a lot of their audience, that not only are they skilled at those techniques demonstrated, but that they are skilled at different kinds of data science problem statements as well. My own suggestion to data science aspirants is that they will be under pressure to demonstrate some of their more involved skills, not merely the ability to use pre-built libraries to solve problems using one’s own basic skill sets in statistical learning, but, perhaps be able to build such algorithms and systems from scratch. This kind of deeper skill is what differentiates the wheat from the chaff in data science.

Concluding Remarks

In conclusion, I believe the mentor’s role in data science has changed – mentors today have their tasks cut out, when it comes to building deeper skill in their data scientists – they should emphasise technical acumen first and foremost, problem understanding and characterisation next, and problem solving effectiveness after this. This builds up a well-layered skill-set where technical skills can perform a harmonious dance in amalgam, resulting in true value to the data science market.

A View of DevOps from the World of Data Science

Operations management as a discipline has taken many shapes and forms in different industries over the years, but there is perhaps something unique that is discussed in software development operations, commonly referred to as DevOps. Many of these considerations around DevOps also apply to the related and increasingly interesting subset of problems that is MLOps, which is the field of Machine Learning Operations. So, what is unique about DevOps and the discussions of software development operations in this context?

Perceptions of DevOps Today And Contrasts with Traditional Industry Operations

One of the tweets I came across recently by a manager hiring for DevOps roles was this one, that sparked an outpouring of ideas from me. The entire thread is below, with the original tweet for context.

Popular understanding of DevOps seems to revolve around tools. Tools for managing code, workflows, and applications for helping with this or the other thing encountered in the context of software development workflows. Strangely enough, operations management in industries that are more established, such as the manufacturing industry, oil and gas or energy industry, or telecommunications tend to have the following sets of considerations:

  1. People considerations: From the hiring and onboarding of talent for the organization, to the development of these as productive employees, to employee exit. Operational challenges here may be the development of role definitions, establishing the right hierarchy or interactions for smooth operations, and ensuring that the right talent is attracted and retained in the organization.
  2. Process considerations:  All considerations spanning the actual process of value delivery, whereby the resources available to the organization are put to use to efficiently solve day-to-day problems and meeting customer requirements on an ongoing basis. Some elements of innovation and continual improvement would also fall into the ambit of the process management that’s part of Operations.
  3. Technology considerations: All considerations spanning the application of various kinds of technology ranging from the established and mundane, to the innovative and novel – all of these could be considered a part of technology management within Operations in traditional organizations.

Anyone familiar with typical, product-centric or services-oriented software development organizations will observe that the above three considerations are spread out among other supporting functions of these organizations. Perhaps technically centred organizations with very specific engineering and development functions evolve this way, and perhaps there is research to show for this hypothesis. However, the fact remains that what is considered development operations doesn’t normally involve the hiring and development of talent for product/solution engineering or development, or the considerations around the specific technologies used and managed by the software developers. These elements seem to be subsumed by human resources and architects  respectively.

Indeed, the diversification of roles in software development teams is so prolific that delivery managers (of the so-called Scrum teams) are rarely in charge of the development operations process. They’re usually owners for specific solution deliverables. The DevOps function has come to be seen as a combination of software development and tooling roles, with an emphasis on continuous delivery and code management. This isn’t necessarily a bad thing, and there is a need for such capablities  – arguably there is a need for specialists in these areas as well. But here’s the challenge many managers hiring mid-senior professionals for managing DevOps:

Cross Functional DevOps and Lean in Manufacturing Operations

When we have DevOps engineers and managers only interested in setting up pipelines for writing and managing code, rather than thinking holistically about how value is being delivered, and whether it is, we miss crucial opportunities for continuous improvement.

As someone who has worked in both manufacturing product development and software product development teams, I find that there needs to be a greater emphasis in software development organizations on cross-functional thinking, and cross-functional problem solving. While a lot of issues faced by developers and engineers in the context of product or solution development are solved by technical know-how and technical excellence, there are broader organizational considerations that fit into the people, process and technology focus areas, that are important to consider – and without such considerations, wise decisions cannot be taken. A lot of these decisions have to do with managing waste in processes – whether that is wasted effort, time or creativity, or technical debt we build up over time, or redundancy for that matter. The Lean toolbox, which originated from the manufacturing industry, provides us a ready reckoner for this, titled the “eight wastes in processes”: inventory, unused creativity, waiting, excess motion, transportation, overproduction, defects and overprocessing. Short of seeing all development activities through these “waste lenses”, we can use them as general guidelines for keenly observing the interactions between a developer, his tools, other developers, and code. Studying these interactions could yield numerous benefits, and perhaps such serious studies are common in some large enterprise DevOps contexts, but at least in the contexts I’ve seen, there’s rarely discussions of this nature with nuance and deep observation of processes.

In fact, manufacturing organizations see Lean in a fundamentally different way from how software development teams see it.

Manufacturing organizations heavily emphasize process mapping, process observations and process walks. And I shouldn’t paint all manufacturing organizations by the same brush, because indeed, the good and the bad ones in this respect are like chalk and cheese – they’re poles apart in how well they understand and deploy efficient operational processes through Lean thinking. Many may claim to be doing Six Sigma and structured innovation, and in many cases, such claims don’t hold water because they’re using tools to do their thinking.

Which brings me to one of the main problems with DevOps as it is done in the software development world today – the tools have become substitutes for thinking, for many, many teams. A lot of teams don’t evaluate the process of development critically – after all, software development may be a team sport, but in a weird way, software developers can be sensitive to replay and criticism of their development approaches. This is reminiscent of artisans in the days before mass production, and how they developed and practised an art in their day to day trade. It is less similar to what’s happening in large scale car or even bottle manufacturing plants around the world. Perhaps there are good reasons for this too, like the development of complexity and the need for specialization for building complex systems such as software applications, which are built but once, but shipped innumerable times. All this still doesn’t imply, however, that tools can become substitutes for thinking about processes and code – there are many conversations in that ambit that could be valuable, eye-opening elements of any analysis of software development practices.

MLOps: What it Ought to Include

Now I’ll address machine learning operations (MLOps) which is a modern cousin of DevOps, relevant in the context of machine learning models being developed and deployed (generally as some kind of software service). MLOps have come to evolve in much the same we saw DevOps evolving, but there is a set of issues here that go beyond the software-level technicalities, to the statistical and mathematical technicalities of building and deploying machine learning systems.

MLOps workflows and lifecycles appear similar to software development workflows as executed in DevOps contexts. However, there ought to be (and are) crucial differences in how these workflows are different between these two disciplines (of software engineering and machine learning engineering).

Some of the unique technicalities for MLOps include:

  1. Model’s absolute performance, measured by metrics such as RMSE or F1 score
  2. Model deployment performance against SLAs such as latency, load and scalability
  3. Model training and retraining performance, and scalability in that context
  4. Model explainability and interpretability
  5. Security elements – data and otherwise, of the model, which is a highly domain-dependent conversation

In addition to these purely technical elements of MLOps, there are elements of the discipline in my mind, that should include people and processes:

  1. Do we have engineers with the right skills to build and deploy these models?
  2. Have we got statisticians who can evaluate the underlying assumptions of these ML models and their formulation?
  3. Do we have communication processes in the team that ensure timely implementation of specific ML model features?
  4. How do we address model drift and retraining?
  5. If new training data comes from a different region, can it be subject to the same security, operational and other considerations?

There may be more, and some of you reading this, who happen to have deployed and faced production scale ML model development/deployment challenges, may have more to add. MLOps should therefore see significant discussions around these elements, and these and other related discussions should happen early and often, in the context of ML model deployment and maintenance.

Finishing up the Columbia University + Emeritus Post Graduate Diploma in ML and AI

I have had a very interesting nine months or so deepening my fundamental skills and learning new skills in AI and Machine Learning. With the Coronavirus crisis and the associated disruption to all our lives across the world in a matter of mere months, it seems like the world has changed overnight. It is now more than ever that skill development and improvement can sustain us all through tough times and unforeseen challenges.

In this context, the PGDMLAI program at Columbia, in retrospect, was a well crafted program that pushed my boundaries. It developed true new skill and changed the way I think about AI and ML problem statements, despite being an active industry professional for several years in the AI and ML space.

On the AI side, most of my new explorations have been in the realms of advanced deep learning and reinforcement learning. In the last month or two, for example, I’ve explored FaceNet, MTCNNs, GANs and DeepRL techniques. In this context, learning about search techniques, Markov decision processes, CSPs and reinforcement learning techniques (policy and value iteration methods) in this program, was particularly rewarding.

Learning Experience, Faculty and Assignments

The course content is highly mathematically detailed and well paced. There is an emphasis on the core ideas of each algorithm you learn, and the assumptions you make in each case. Interesting concepts and sub-problems such as transformations for monotonic functions, two-class problems in SVMs, the maximum likelihood principle, Bellman’s equation, etc., are discussed in context, and additional resources there gave me an opportunity to dig into this content too when I had gotten done with the course content or assignments.

As for pre-requisites, it goes without saying that even for me as an industry professional in AI and ML who’s involved on a regular basis in the development of AI and ML code and solutions, the depth of ideas presented is advanced – it is a graduate level course. I needed some study pre-lecture and revision and revisiting ideas post-lecture to understand certain concepts correctly. At times, I’d feel out of depth and would need to revisit course videos several times and sometimes even pre-requisites. In my case, I went back quite a bit to linear algebra lessons from MIT OCW (Gilbert Strang’s lectures) and elsewhere. I also went back to a lot of Python programming on DataCamp, which all students in the course had access to.

In addition to the core topics within AI and ML, several interesting topics in CSPs and Reinforcement Learning were also discussed. Cryptarithmetic puzzles that are solved by graph search techniques like backtracking search, and the Sudoku solvers stand out. Also, for someone like me without a formal background in computer science, the initial lectures on graph search algorithms were gold, they were essential to understand the deeper ideas within AI eventually.

A related thread from my Twitter page:

I want to take a moment to appreciate the incredible faculty and staff for this course:

  1. Prof. David Yakobovitch – the course leader for the program who shared invaluable knowledge in all the webinars
  2. Prof. Ansaf Salleb Al-Aoussi – the AI expert who taught various search and ML techniques lucidly and clearly
  3. Prof. Jacob Koehler – all the excellent office hours sessions that were incredibly helpful in clarifying hard problems in implementing solutions we’d learnt conceptually
  4. Prof. John Paisley – who provided lucid and mathematically comprehensive lectures in machine learning

All above staff (and many others) routinely engaged with many of us students on the course forums, and I am sure I’ve made a few friends among the students during the course of this program.

I also want to thank the Emeritus and Columbia team for making DataCamp’s data science and ML courses available to all students. This definitely helped me in the course of the learning experience.

As someone who has been coding up ML algorithms in Python for years, some of the content wasn’t new to me, but that didn’t keep the assignments from being challenging or interesting. A whole lot of the program, especially the pieces around reinforcement learning, search and also many ML algorithms (KNNs, K-Means, Linear and Logistic regression, regularized regression methods, and more) from scratch. During this program, I picked up some more Tensorflow and Keras than I already knew, and also picked up PyTorch!

Time (and Energy) Management 

The course took many weekend sessions and evenings over the last 9 or so months to complete. I would eagerly await some of the break weeks sometimes to keep pace – and the weekly exercises were comprehensive and not just coding assignments at one level – they were full fledged problem solving opportunities. 

Being in a full time job, and managing responsibilities at home besides learning ML and AI requires good time management at some level. I definitely could have done some things better, looking back – after all, nobody is perfect! If I were to list three things I could have done better they’d be: a) sticking with the assignment problems everyday and trying different approaches, b) writing my own sub-problems to solve the assignment problem at hand, and c) Setting aside time to read and replicate papers related to the topics at hand.

Capstone Project Experience

The final part of the program was a capstone assignment, featuring some particularly dirty and complex data. The assignment emphasized the importance of extracting business value from data. This aspect of data science is unchanged over years – it is as it was in 2015 when I first took up data science. Value from data is where the rubber hits the road for organizations adopting data science and ML/AI. In the last nine or so months at work, while on this program, I’ve helped build a serverless data lake on the cloud, I’ve helped solve many large scale machine learning problems for telecommunications networks – and in all these cases, as in the capstone, it came back to “How do we tie the results from our analysis to the business decisions to be taken?”

In this sense, the Capstone project was interesting and challenging – like in real world projects, you have to make and state assumptions, qualify and confirm some of these assumptions, and ensure your analysis makes sense. You’d have to go back and redo some of the analysis based on new data that comes to light. You’d have to spend a significant amount of time planning your data preparation process. For instance, if you’re building a supervised classification model, you’d need to identify the hypotheses, the associated features and budget time for data preparation tasks. A large part of data science is ensuring that your dataset is suitable for machine learning – that you have target variables identified and your features sorted – and this process of developing a data pipeline is as important as any other step of the process.

I was fortunate for my Capstone project submission to be awarded an “Exemplary Assignment” badge by the course faculty (badge below)! I received feedback via a grading rubric, which was also very interesting and meaningful.

EMERITUS_Badge

Onward!

As anyone within the technology industry will know, learning new things is a regular part of our lives and our mental flexibility in learning new things takes us forward in our careers. This is even more the case with data, machine learning, AI and related areas of technology today. I cherish the learning experience within the PGDMLAI as with others in the past, and treasure it as much as a real project – in many ways, as a comprehensive, well put together program, it was a great way to pick up in-depth skills and solve challenging problems. The proverbial axe has, therefore, been sharpened. 

I hope to spend even more time on reinforcement learning centric problem statements in the coming months. Topics such as GANs and the associated new challenges they present are also interesting. The current Coronavirus disease crisis has given me opportunities to think about the problems that need to be solved in the world today, and new and innovative ways in which data and AI can be used to solve these problems. I look forward to ideate and determine such interesting problems and find ways to extend my newly gained skills to these problems, be they in domains as diverse as healthcare, pharma, telecommunications, manufacturing or technology.

 

 

Exploring SVM Kernels

Support Vector Machines are a popular option for data scientists wanting to explore and model higher dimensional data. Despite their lack of scalability, they’re popular for prototyping different kinds of classifiers for systems where there are large numbers of variables. At the core of the SVM is the use of a kernel function, which enables a mapping of the feature space to a higher dimensional feature space. Therefore, if we’re unable to find separability between classes in the (lower dimensional) feature space, we could find a function in the higher dimensional space, which can be used as a classifier.

Two classes of data in the R^2 space

In this Jupyter notebook I’ve explored a couple of different types of kernel functions for bivariate, two-class data, where an SVM is being used to separate out these classes. Since these classes are not linearly separable, the use of kernel functions here enables us to find the best possible hyperplanes that can solve the separability problem. What’s interesting to note is that the convex hulls (in this case, polygons) for these classes are overlapping in the 2D space. This is a clear indicator of a lack of linear separability.

The blue polygon here represents the convex hull for one class, which is dimensions-wise nested in another, in this data representation

The use of a kernel opens up the possibility of linear separability, since we add an additional spatial dimension on which these points get distributed. Specifically here we have two different kernel functions that are explored:

\phi(x_{1}, x_{2}) = (x_{1}^2, x_{1}x_{2}, x_{2}^2)^{T}

K(x_{1}, x_{2}) = a e^{{-\frac{1}{b} ||x_{1} - x_{2}||^{2} }}

The latter is called the radial basis function kernel, or the RBF kernel. Visualizing this kernel for the data we’d generated gives us the following nice image. What’s easily visible here is the possibility of separating out the classes thanks to the additional rbf dimension that has now been added.

RBF (vertical axis) enables separation of the two classes, in blue and yellow.
Note: Low opacity used for better visibility
Decision boundary identified by the SVM (which uses the RBF kernel)

Upon training the SVM classifier, visualizing the results gives us the below plot. The thick grey line is the decision boundary that enables us to separate the originally linearly inseparable classes in the dataset.

There are other explorations I hope to do on this notebook in future, specifically the process of calculating the sign (class label) of a dataset, based on the Lagrangian – which indeed brings us to the idea of the SVC being a maximal margin classifier. This is also referred to as the dual problem of the SVM. For another post!

Statistical Competence and Its Importance for Good Data Science Careers

In 2019, enterprises routinely begin initiatives related to analytics, data science and machine learning that invoke specific technologies from a very early stage in their initiatives. This tendency to put technology ahead of value sometimes extends to analytics champions and managers who take up or lead data-intensive initiatives. While this may seem pragmatic at one level, at another level, it may lead to significant problems when ensuring successful outcomes from such analytics initiatives and programs. In this post, I’ll address the three-pronged conundrum of statistical competence in the data science world, specifically in the context of data science consulting and services, and specifically what it means for the careers of data science candidates now and in the future.

Hiring Statisticians: An Expert’s View

Kevin Gray is one of my connections on LinkedIn who posts insightful content on statistical analysis and related topics on a regular basis, including very good recommendations for books on various statistical and analytical techniques and methods. One of his recent posts was an article he’d authored titled “What to Look For in a Statistician” (the article, and my comment), which definitely resonated with my own experiences in hiring statistically competent engineers in different settings, such as data science and machine learning, between 2015 and today. In years past, I have had similar experiences when hiring competent product engineers and manufacturing engineers in data-intensive problem solving roles.

The importance of statistical thinking and statistical analysis in business problem solving cannot be underestimated. However, even good advice that is canon, and that is well-acknowledged, often falls on deaf ears in the hyper-competitive data science job market. Both hiring managers and recruiters tend to emphasize keywords comprising the latest framework or approach, over the ability to think critically about problem statements, carefully architect systems, and rigorously apply statistical analysis and machine learning to real world problems while keeping considerations of explainability in mind.

The Three-Pronged Conundrum of Data Science Talent

Now you might ask why I say this, and what I really mean by this. The devil, as they say, is in the details, and one essential problem with the broad and wide proliferation of tools, frameworks and applications of high capability, that can perform and automate statistical analysis of different kinds, is the following three-pronged conundrum:

  1. Lack of core statistical knowledge despite having a working knowledge of the practicum of advanced techniques: Most candidates in the data science job market who are deeply interested in building data science and ML applications have unfortunately not developed skills in the core statistical sciences and statistical reasoning. Since statistics is the foundation for machine learning and data science, this degrades the quality of projects and programs which have to rely on hiring such talent. When they prefer to use software to do most of or all the thinking for them, their own reasoning about the problem is rarely good enough to critically evaluate different statistical formulations for problems, because they think in very set and specific ways about problems thanks only to their familiarity with the tools.
  2. Tools as an unfortunate substitute to statistical thinking: Solutions, services and consulting professionals in the data science and advanced analytics space, who have to bring their best statistical thinking to client-facing interactions, are unable to differentiate between competence in statistical thinking, and competence in a specific software tool or approach.
  3. Model bloat and inexplicability: The use of heavy, general purpose approaches that rely on complex, less explainable models, than reliance on simpler models that are constructed upon a fuller understanding of the true dynamics of the problem.

These three sub-problems can derail even the best envisioned data science and machine learning initiatives in product / solution delivery firms, and in enterprises.

Some “Unsexy” Characteristics of Good Data Scientists

These are also not “sexy” problems – they’re earthy, multi-dimensional, real world problems that have many contributing factors, from business and how it is done, to the culture of education and the culture of software and solution development teams. Kevin Gray in his post touches upon attitudinal qualities for good statisticians, which could also be extended to data science leaders, data scientists and data engineers:

  1. Integrity and honesty are important in data science – this is true especially in a world where personal data is being handled carelessly and sometimes gratuitously by many applications without heed to data protection and privacy, and when user data is taken for granted by many technology companies. This is not an easy expectation or evaluation point for hiring managers, since it is only long association with anyone which allows us to build a model of their integrity, and rarely does one effectively determine such an attribute in short interviews. What’s dismal about data science hiring sometimes, is the proliferation of candidate resumes which are full of fluff, and the tendency of candidates to not stand up to scrutiny on skills they identify as “key” or “core” skills.
  2. Curiosity and a broad spectrum of interests – this cannot be understated in the context of a consulting data science or machine learning expert. The more we’re aware of different mental models and theoretical frameworks of the world and the data we see in it, the better we’re able to reason starting from hypotheses about the data. By extension, we’re better able to identify the right statistical approaches for a problem when we start from and explore different such mental models. The book I’ve linked to here by Scott E. Page is a fantastic evaluation of different mental models. But with models come biases, to restate George E. P. Box’s famous quote, “All models are wrong, some models are useful”.
  3. Checking for logical fallacies is key for data science reasoning – I would add to the critical thinking element mentioned in Kevin’s post, by saying that it behooves any thought leader such as a data science consultant to critically evaluate their own thinking by checking for logical fallacies. When overlooked, a benign piece of flawed reasoning can turn into a face-melting disaster. The best way to ensure this does not happen is to critically evaluate our ideas, notions and mental models.
  4. Don’t develop one hammer, develop a tool box – Like experienced plumbers, carpenters or mechanics, the tools landscape of a data scientist today should not be one of quasi-religious fervor in promoting one technique at the cost of others, such as how deep learning has come to be promoted in some circles as a data science panacea. Instead, the effective data scientist is usually pragmatic in their approach. Like a tailor or carpenter who has to cut or join different materials with different instruments, data scientists today do not have the luxury of getting behind one comfortable model of thinking about their tool set and profession – and any attempt to do this can be construed as laziness (especially for the consulting data scientist) at best. While the customer is always right, there are times when the client can be wrong and it is at these times that they need the advice of a qualified statistician or data scientist. If there is one time when data scientists should not abandon their statistical thinking, it is this kind of a situation.

Concluding Remarks

To conclude, data scientists ought not to be seen as resources that take data, analyze it using pre-built tools, and write code to explain the data using pre-built libraries of various kinds. They’re not software jockeys who happen to know some statistics and have a handle on machine learning workflows. Data scientists’ work scope and emphases as industry professionals and consultants go way beyond these limited definitions. Data scientists are expected to be dynamic, statistically sound professionals who critically evaluate real world problems based on theories, data and evidence drawn from many sources and contexts, and progressively build a deeper understanding of these real world problems that lead to tangible value for their customers, be they businesses or the consumers of products. The sooner data scientists realize this, the better off they will be while charting out a truly successful and fulfilling data science career.

Understanding the Logarithm Trick in Maximum Likelihood Estimation

Maximum Likelihood Estimation is a fundamental and powerful idea that’s at the centre of many things we do with data – so much so that we often use it without knowing it. MLE allows us to find a model’s parameters that are likely to enable the model to represent the data we have on our hands as closely as possible. This short post addresses the logarithm trick which is used to enable simpler MLE calculation.

There are two elements to understanding the formulation of MLE for the common Multivariate Gaussian model (which could be extended to other models equally):

  1. The i.i.d assumption that simplifies the MLE formulation
  2. The logarithm trick with enables solution of the MLE formulation

On this blog I’ve discussed topics like time series analysis in the past where the idea of independent and identically distributed variables is addressed, and of course, being an important statistical topic, is is well explained and understood. The logarithm trick, however, is specific to the simplification and solution of MLE formulations, and is helpful to understand.

The logarithm function very simply enables scale variance in any input data while allowing location invariance. This is extremely helpful when dealing with monotonic input data that we want to ensure continues to be monotonic after transformation, but whose scale we want to change.

When building a model p ( x_1,.... x_n | \theta ) of the data (x_1,.... x_n), the MLE formulation seeks to find the appropriate values of \theta such that

\bigtriangledown_{\theta} \prod_{i = 1}^{n} p(x_i | \theta ) = 0

The interesting thing about the log transform is, as I said earlier that in the transformation ln ( \prod_{i} f_i ) = \sum_{i} ln(f_i) , there is no change in where f_i may attain a maximum or a minimum when it is transformed to ln(f_i) for any i. This logarithm trick enables us to compute the latter product more simply, and thereby execute the MLE.

Questions that Data Scientists Hate Getting

This is a variation on a Quora answer.

When asked how data scientists can be effective, there are a few things that com e to mind:

  1. Skills: A curiosity and sufficient skill in data analysis methods and techniques
  2. Fundamental needs: the data and access to the tools to perform analysis — and this would include the environments
  3. Performance needs: Sufficient resources, time and good enough processes to validate or invalidate hypotheses and build models based on them
  4. Excitement needs: Sufficient support and latitude to independently deploy projects based on successful hypotheses tested and models built

Note that while these criteria listed above begin with the fundamental skills required to do data science, the focus shifts in items 2, 3 and 4, to what is required for data scientists to be effective. The first of these are the fundamental needs, such as the data itself, and the access to the required tools, be they statistical or machine learning tools, databases, visualization libraries, or other resources. The second of these are the performance needs, which will help the data scientist do whatever it is that they do, a bit better than how they’re doing this now. This includes processes and systems that enable the data scientist to improve their own capabilities. Finally, we have excitement needs, which enable data scientists to do outstanding work — a large part of this is being able to reuse what has been built, through deployment of various kinds.

It is in this context that we can discuss how managers of data science teams can help them be effective.

If there is one kind of behaviour in analytics managers that I wish changed, it is the one I describe in the following lines.

A lot of what data scientists do is experimental, throw-away analysis. However, it is tempting for a number of managers (many of who have made up their minds that some hypothesis holds true, or will work), to assume that they’re right, and what is required from the data scientist is the detailed model that formalizes the relationship.

This kind of assumption makes for poorly designed projects, and doesn’t amply use the data scientist’s time for exploratory analysis, for evaluating the development of different kinds of models, and for finding out what works, given the dataset.

Naturally, given the time-bound nature of businesses and poor understanding of analytics at the executive level in many organizations, such clients are commonplace, and such managers also find themselves in a situation where they push for results without the right underlying systems, data or resources. Sometimes, they begin projects with data scientists who lack the specific skills to build the kinds of models required to solve problems. While this may be the case, the challenge many data scientists in business and consulting have is dealing with such unreasonable expectations.

In this specific context, some questions that shouldn’t be posed to data scientists might be along the following lines:

  • “Assuming that hypothesis X works, how long would it take to build a full fledged application using this hypothesis X?”
  • “The domain experts are convinced that this hypothesis X is true. Why don’t your results reflect this too?”
  • “The values of R_sq or precision/recall I see here don’t reflect what can be done with the data. Aren’t better results possible?”

These kinds of questions are simplistic when in the initial stages of a data science activity/experiment, and in some situations, they could be dangerous too (although they’re innocuous mistakes any manager new to analytics initiatives may make).

For the same reason that “a little knowledge is a dangerous thing” these project managers might be playing with the fortune of the entire analytics program they serve, because they base even large projects on such naive and unverified assumptions. Were they to change their behaviour by giving due consideration to exploratory data analysis, and what the data actually says about viable models and applications that may be built, they might be putting their data scientists and engineers on the path to success.

A Simple CNN Tutorial in Keras

In the last year or so, I have begun working extensively with Keras, Tensorflow and CNTK for various problems at work in industries ranging from manufacturing, to media, to cybersecurity.

Here is a simple convolutional network tutorial on Kaggle that I developed in Keras and Tensorflow. Given the GPU-enabled kernels you have within Kaggle these days, it has become easy enough to train large scale image data on some of these kernels. Performance is another matter, though, since the Tesla K40 GPUs you get here are the lower end GPUs, and are also load balanced for multiple users. In any case, it allows you to even try out CUDA code – and that opportunity can’t be beat, given the low cost of doing Kaggle.

My motivation for putting together a tutorial is not the dearth of tutorials – there are enough and more out there. However I wanted to emphasize certain good practices here, and intend to continue to update the kernel in question in future to illustrate those.

Caveat: The internet is awash with tutorials on deep learning using these frameworks, so I won’t dwell much on why this tutorial is different, because it isn’t very different. That said, it does emphasize how a simple deep learning model could be made more effective by using various good practices, such as batch normalization, some explanations about loss functions, and some amount of data exploration in the context of data and labels for this supervised problem.

Achieving Explainability and Simplicity in Data Science Work

This post stems from a few of the tweets I’d authored recently (Over at @rexplorations) on deep learning, data science, and the other skills that data scientists ought to learn. Naturally, this is by no means a short list of skills, given the increasingly pivotal role that data scientists play in organizations.

Here’s a summary of the tweet-stream I’d put out, with some additional ponderings.

  1. Domain knowledge is ignored on the data science road to perdition. Doing data analysis, or building models from data without understanding the domain and the relevance of the data and factors one is using for these models, is akin to “data science suicide”. It is a sure shot road to perdition as a data scientist. Domain knowledge is also hard to acquire for data scientists, especially those working on projects as consultants, and applying their skills in a consultative, short-term setting. For instance, I have more than a decade of experience in the manufacturing industry, and I still find myself learning new things when I encounter a new engineering set up or a new firm. A data scientist is nobody if not capable of learning new things – and domain knowledge is something that they need to constantly skill up on, in addition to their analytical skills.
  2. Get coached on your communication skills, if needed. When interacting with domain experts and subject matter experts, communication skills are extremely important for data scientists. I have frequently seen data scientists suffer from the “impostor syndrome” – not only in the context of data analysis methods and techniques, but also in the context of domain understanding.
  3. Empathise, and take notes when speaking to subject matter experts. It is for this reason that the following things are extremely important for new data scientists interacting with subject matter experts:
    1. Humility about one’s own knowledge of a specific industry area,
    2. An ability to empathise with the problems of different stakeholders
    3. The ability to take notes, including but not limited to mind maps, to organize ideas and thoughts in data science projects
  4. Strive for the usefulness of models, not to build more complex models. Data scientists ignore hypotheses that come from such discussions at their own peril. Hypotheses form the lifeblood of useful data science and analysis. As George E. P. Box said, “All models are wrong, some models are useful” – and this couldn’t be more true than when dealing with models built from hypotheses. It is such models that become really useful.
  5. Simpler models are easier to manage in a data ethics context. In product companies that use machine learning and data science to add value to customers, a debate constantly exists on the effective and ethical use of customer data. While having more data at one’s disposal is helpful for building lots of features, callous use of customer data can present a huge risk. Simpler models are easier to explain – and are arrived at when we accumulate sufficient domain knowledge, and test enough hypotheses. With simpler models, it is easier to explain what data to collect, and this can also help win the customer’s trust.
  6. Careful feature engineering done with human supervision and care may be more effective and scrupulous than automated feature engineering. We live in a world where AutoML and RoboticDataScience are often discussed in the context of machine intelligence and speeding up the process of insight generation from data. However, for some applications, it may be a better idea in the short term to ensure that the feature engineering happens through human hands. Such careful feature engineering may give organizations that use sensitive data a leg up as a longer term strategy, by erring on the side of caution.
  7. Deep learning isn’t the end of the road for data scientists. Deep learning (justifiably) has seen a great deal of hype in the recent past. However, it cannot be seen as a panacea to all data analysis. The end goal from data is the generation of value – be it for a customer, or for society at large. There are many ways to do this – and deep learning is just one approach.

I’m not discussing the many technical aspects of building explainable models. These technical aspects are contextual and depend on the situation, for one, and additionally, the tone of the post and tweets are lighter, to encourage a discussion and to welcome beginner data scientists to this discussion. Hence my omission of these (important) topics.

If you like something on this post, or want to share any other related insights, do drop a comment, or tweet to me at @rexplorations or message me at LinkedIn.