Newsletter Issue 10: Overview of MLOps, ML Pipeline, and ML Maturity Levels for continuous training, integration, and deployment.
Best sources to find free real-world public datasets for your machine learning and data science projects.
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.
Newsletter Issue 2: Is the model's performance score enough? Testing an ML model is not the same as software testing.
How to collect data and extract actionable insights from descriptive, diagnostic, predictive, and prescriptive data analytics using the drivetrain approach
Introduction to data science, machine learning, and deep learning for software engineers and developers, with curated online resources.