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. 

Databricks recently held a webinar on how they worked with Virgin Hyperloop One engineers.

They discuss the goals, implementation, and outcome of moving from Pandas code to Koalas code and using MLflow. Lots of code, notebooks, demos, etc.

Come hear Patryk Oleniuk, Software Engineer at Virgin Hyperloop (VHO) discuss how VHO has dramatically reduced processing time by 95%, while changing less than 1% of previously single-threaded, pandas-based python code. Attendees of this webinar will learn:

How VHO leverages public and private transportation data to optimize Hyperloop designHow to ‘Sparkify’ (scale) your pandas code by using ‘Koalas’ with minimal code changesHow to use ‘Koalas’ and MLflow for sweeping machine learning models and experiment resultsFeatured SpeakersPatryk Oleniuk, Lead Data Engineer, Virgin Hyperloop OneYifan Cao, Senior Product Manager, Databricks 



Koalas Notebook:

Machine Learning can be confusing sometimes.

From the esoteric terms to elevated expositions it seems like a terribly difficult area to get into.

Seth Juarez, like me, started off as a developer, and he tackles the one term that is used all of the time in Machine Learning: the elusive “model.

From the description:

First we set up how machine learning is different, how to think about it, and finally what a model actually is (spoiler alert – think “a function written a different way”). Would love your feedback