About Me — ToC of Medium Articles

Satish Chandra Gupta
Author’s caricature drawing by Manorama Maharana

List of My Medium Articles

  • Product: Product Design & Management encompasses the whole gamut of things: identifying the user and business needs, designing the user experience (including implicitly or explicitly collecting feedback from the use of ML-assisted features), defining business success metrics, and guiding the whole journey from conception to delivery. It is one of the hardest parts and key to the success of the ML-assisted feature/product. Sadly, it is often ignored.
  • Data: Data Engineering takes care of collecting, curating, storing, and managing the needed data at scale (aka Big Data). As Monica Rogati explained in the Data Science Hierarchy of Needs, data engineering covers the first 2 layers (out of 6) of the pyramid. Anaconda State of Data Science 2021 report says that a good 39% of the effort goes into data cleaning and data preparation (page 14). Without good quality data, there is no ML. And without a solid Data Engineering foundation, there is no ML product. Data Pipelines are the railroads on which heavy and marvelous wagons of ML run.
  • ML: The spectrum of data analytics, data science, and machine learning that covers designing statistical/probabilistic models vs. traditional deterministic algorithms/programs. In ML, data is logic, and some of the product features are implemented using statistical models.
  • Dev: Developers knit an ML model seamlessly into the rest of the product, and continuously develop-test-deploy code to achieve business goals. They apply the rigor of software engineering principles to design, develop, test, evaluate, and maintain software systems. They scale a model for mass consumption.
  • Ops: Operations (DevOps or DevSecOps) is the discipline of continuous integration and continuous delivery/deployment (CI/CD). In the case of ML, it becomes CT/CI/CD: continuous model training, integration, and delivery/deployment. The aim is to automate the process of training models, integrating and packaging them into software services (typically docker containers), deploying them on the cloud, monitoring their performance in production (e.g. catching concept/data drift), firing alerts in case of issues, triggering rollbacks or retraining as and when needed.

[Data] Date Engineering

[ML] Machine Learning

[Dev] Software Engineering

[ML4Devs] Machine Learning for Developers — Biweekly Newsletter

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