Survey of data science and machine learning lifecycle from resource-constrained batch data mining era to current MLOps era of CI/CD/CT at the cloud scale.
MLOps Lifecycle brings model and software development together for continuous integration, deployment, and training (CI/CD/CT) of models in ML products.
Newsletter Issue 11: You define logic in traditional programs. Machine Learning extracts logic (models) from the data.
Newsletter Issue 8: Why machine learning and data science projects fail, and what you can do to void it.
Newsletter Issue 3: Arguments against and for embracing Agile in data science and machine learning projects.