Models

Databricks

MLflow Model Serving in Databricks

Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. In this video from a Data + AI Summit Europe 2020 Meetup, Andre Mesarovic introduces […]

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Databricks Machine Learning

What’s New in MLflow? Accelerating the Machine Learning Lifecycle

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 […]

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Databricks

Scaling Pandas with Apache Spark + Koalas for ML

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 […]

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Machine Learning

Machine Learning Models

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 […]

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