Don't put data science notebooks into production

We've come across many clients who are interested in taking the computational notebooks developed by their data scientists, and putting them directly into the codebase of production applications. Data science ideas do need to move out of notebooks and into production, but trying to deploy that notebooks as a code artifact breaks a multitude of good software practices. Predictably, that results in a number of observed pain points. This behavior is a symptom of a deeper problem: a lack of collaboration between data scientists and software developers.

18 November 2020

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David Johnston

David has over 20 years of experience working in data science, artificial intelligence, optimization and other areas of science and technology. He has over 8 years of experience as a data science consultant at ThoughtWorks and has worked in research positions at top US universities, government laboratories and NASA. David brings a wide range of expertise in data science related areas and has a strong focus on integrating data science into software applications to solve client problems in more effective ways. He is also a primary contributor to CD4ML, a starter kit for building machine learning applications with continuous delivery.

First, let’s describe what computational notebooks are. Notebooks originated with the Wolfram Mathematica language and the idea is now quite popular in the data science community, particularly with Python and R users. Basically, it's a combination of a script consisting of commands integrated with some visualization and documentation. You see the code that has been run and the result, whether it is just text, a nicely formatted table or a graphical figure. The documentation can explain what is happening, making them useful for tutorials. Notebooks are essentially a nicer interactive shell, where commands can be stored and easily rerun with changes. The graphics or outputs are right there in one window rather than saved elsewhere in files or popped up in other windows. The interactive session can be saved in one file and shared so that anyone else (under certain conditions) can run it with the same results.

Notebooks are essentially good at two things. They make a nice interactive shell for data scientists doing interactive, exploratory work. They are also good for demos. They are not crucial tools for doing data science and many data scientists do not use them at all.

Notebooks share a lot of characteristics of spreadsheets and have a lot of the same strengths and weaknesses. First, the strengths. They allow people without much in the way of programming skills to do useful quantitative work. Notebooks are essentially scripts and scripting is the first step in general programming. Excel, for example, allows for scripting as well, such as using formulas. And one can actually do a whole lot of useful work with drag and drop operations as well. They both are tools that combine the concerns of storage (both code and data), visualization, and bussiness logic into one application.

Having one tool being the one-stop-shop for several concerns has both advantages and disadvantages. The advantage is simplicity for simple things. Why would I use a database, a Java application and Javascript frontend just to do some simple operations to calculate the payroll for the dozen employees that I employ at my startup? That’s what spreadsheets are great at. But that doesn’t mean a spreadsheet should be used to handle payroll for a major international bank. And they are not used for that, for good reasons. Those situations are more complex. There are many more variables. They have auditing requirements. The data may be quite large, etc.

Teams of people can succeed at building large applications to solve complex problems but only if they can control that complexity. And we have very few tools to do that. The most important of all is to break it into many smaller, less coupled problems. That’s why in the Presentation Domain Data Layering pattern, we separate UI, domain logic, and storage. We can focus on how a calculation is performed without being distracted by how it will be displayed or how data is accessed.

Putting a notebook into a production pipeline effectively puts all the experimental code into the production code base. Much of that code isn't relevant to the production behavior, and thus will confuse people making modifications in the future. A notebook is also a fully powered shell, which is dangerous to include inside a production system. Safe operations require reproducibility and auditability and generally eschews manual tinkering in the production environment. Even well intentioned people can make a mistake and cause unintended harm.

What we need to put into production is the concluding domain logic and (sometimes) visualizations. In most cases, this isn't difficult since most notebooks aren't that complex. They only encourage linear scripting, which is usually small and easy to extract and put into a full codebase. If it's more complex, how do we even know that it works? These scripts are fine for a few lines of code but not for dozens. You’ll generally want to break that up into smaller, modular and testable pieces so that you can be sure that it actually works and, perhaps later, reuse code for other purposes without duplication.

So we’ve argued that having notebooks running directly in production usually isn’t that helpful or safe. It’s also not hard to incorporate into a structured code base. So why is anyone even talking about how to productionize notebooks? The essence of the problem is that data scientists and software developers do not always communicate very well or understand what the other needs to do. Many data scientists do not really understand the concerns of professional software developers such as automated, reproducible, and auditable builds, or the need and process of thorough testing, or the importance of good design in making codebases supportable and flexible. In turn, many software developers do not really understand what data scientists are doing.

While two types of people can often work well together without understanding the details of what the other has to do, this is generally not one of those situations. There are tremendous advantages to be had when data scientists and developers can share knowledge and learn a little more about what the other has to do and why they do things the way they do. This is to say that data scientists should strive to learn software development and work fully embedded in the delivery team responsible for delivery of production software. They don't need to reach full capacity in this regard but they should fully understand the basics and continue to learn in the areas most relevant to their work on the team.

They’ll find that using many of the techniques of software development actually makes them more productive as data scientists. They’ll find they can handle more complex tasks and spend far less time debugging when they structure code properly. Developers will find that they can make much better use of data science models and methods when they take the time to understand a little more about what is actually going on. Neither needs to become fully skilled in the other field but they should at least be competent in its basics.

Notebooks are useful tools for interactive data exploration which is the dominant activity of a data scientist working on the early phase of a new project or exploring a new technique. But once an approach has been settled on, the focus needs to shift to building a structured codebase around this approach while retaining some ability to experiment. The key is to build the ability to experiment into the pipeline itself. An example would be including a machine learning model registry which allows one to modify parameters at either run-time or build-time and stores results such as performance metrics in a data store. This has the advantage that experiments are always repeatable as they run with versioned code and their results are retained for purposes of comparison, and also as demonstrable markers of progress. This ensures that any difference in effect can be demonstrated to come from an intended cause which is the hallmark of any good experiment. The goal, after all, is to learn what changes to production software will create more business value. The smaller the gap between the environment of the experiment and the actual implementation, the more we can be confident that the change really creates value. Another key idea is to build data science pipelines so that they can run in multiple environments, e.g., on production servers, on the build server and in local environments such as your laptop. That enables even more possibilities of experimentation without disrupting anything happening in production.

To conclude, we believe the discussion of how to productionize data science notebooks is missing the point. The goal should be to empower data scientists and their entire delivery teams to come together and build software that delivers the required business functionality while still retaining the ability to experiment and improve. This requires moving out of notebook style development after the initial exploratory phase rather than making it a continuing pattern of work requiring constant integration support. This way of working not only empowers data scientists to continue to improve the working software, it includes them in the responsibility of delivering working software and actual value to their business stakeholders.

Significant Revisions

18 November 2020: published