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.
The evolution from resource-constrained batch data mining to continuous MLOps at the cloud scale, and how to bring model development and DevOps together
MLOps Life Cycle for continuous training, integration, and deployment (CT/CI/CD) of models while building ML-assisted products.
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.
Best places to find free datasets similar to your problem and kickstart your machine learning or data science project.
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 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.