Newsletter Issue 13: Don't fall for the "AI-first" hype. Focus on being "data-first" and ML/AI will naturally follow.
Newsletter Issue 12: How to progressively adopt MLOps, but only as much as justified by your needs and RoI.
Newsletter Issue 11: You define logic in traditional programs. Machine Learning extracts logic (models) from the data.
Newsletter Issue 10: Overview of MLOps, ML Pipeline, and ML Maturity Levels for continuous training, integration, and deployment.
Newsletter Issue 9: Machine Learning is no silver bullet. Here is how you can determine whether ML is. the right tool to solve your problem.
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.