Newsletter Issue 8: Why machine learning and data science projects fail, and what you can do to void it.
Newsletter Issue 7: Universe of MLOps tools and how The Big 3 (Amazon, Google, and Microsoft) think about ML lifecycle and MLOps maturity level.
Newsletter Issue 6: Cheatsheets and decision trees to find the best visualization for your data and purpose
Newsletter Issue 5: Examines the effort-cost spectrum for data collection approaches from do-it-yourself to fully-outsource-it w.r.t. team's ability/budget.
Newsletter Issue 4: 5 steps for devs to learn machine learning: Kaggle micro-courses & competitions, ML Bootcamp, Andrew Ng's ML course, Neural Networks.
Newsletter Issue 3: Arguments against and for embracing Agile in data science and machine learning projects.
Newsletter Issue 2: Is the model's performance score enough? Testing an ML model is not the same as software testing.
Newsletter Issue 1: ML products require 5 disciplines — product design, data engineering, machine learning, software development, and operations.