Issue 7: MLOps lifecycle, maturity levels, and tools — You must have noticed the buzz about MLOps. MLOps is the lifecycle, process, and tools for deploying machine learning models in production. There has been an explosion of MLOps vendors and tools. Many of those are named as xyzFlow or xyzML: KubeFlow MLFlow MetaFlow ZenML BentoML OctoML ClearML These were…

Issue 6: Cheatsheets and decision trees to find the best visualization for your data and purpose — When you see informative charts and infographics, what is your first reaction? I wonder how you decide which chart type to use. In this issue, I will share some of the best resources I found. The Extreme Presentation Method The Extreme Presentation Method by Dr. Andrew V. Abela is probably the most popular Chart…

Issue 5: Pick a data collection setup that suits your organization’s capabilities and budget. — Cliché: Without data, there can be no data science. But it is true. While learning data science, we mostly use public data sets or scrape data off the web. But in ML-assisted products, most of the data is generated and collected through business applications. The first step in any data…

Issue 4: Perfect is the enemy of good. — The most frequent question I get from developers is: what is the best way to get into Machine Learning? A few years back, my response was: Google for best resources and learn Find problems at work and apply what you learn Repeat Though that response was honest and sincere, I…

Issue 3: Should data science embrace Agile? — To be agile, or not to be, that is the question. I guess the answer depends on whom do you ask. I have seen many Data Scientists bitterly oppose Agile and Scrum: 6 Reasons why I think Agile Data Science does not work Why Scrum is awful for data science Why data science doesn’t respond well to Agile methodologies Don’t make…

Issue 2: Model Evaluation vs. Model Testing vs. Model Explainability. — Is the model’s performance score enough? If your machine learning model has a high correctness score on the holdout test data set, is it safe to deploy it in production? All models are wrong, but some are useful. — George E. P. Box (famous British statistician) But the question I am asking is: Are more correct…

Issue 1: ML products require 5 disciplines — product, data, ML, dev, ops. — When we look at Machine Learning through Software Developer’s lens, building a successful ML product or, more often, ML-assisted product features takes 5 disciplines to come together: product, data, ML, dev, and ops. Product Design & Management encompasses the whole gamut of things: identifying the user and business needs, designing…