In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management.

Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking.

Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference APIs for batch and real-time scoring. 

This second part of the tutorial session from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume Lemaitre discuss how to pick an algorithm for your application, how to set its hyper-parameters, and how to evaluate performance.

Link to materials

This tutorial from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn.

Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume Lemaitre discuss how to pick an algorithm for your application, how to set its hyper-parameters, and how to evaluate performance.