Introducing MLflow for End-to-End Machine Learning on Databricks скачать в хорошем качестве

Introducing MLflow for End-to-End Machine Learning on Databricks

Solving a data science problem is about more than making a model. It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps. In this simple example, we’ll take a look at how health data can be used to predict life expectancy. It will start with data engineering in Apache Spark, data exploration, model tuning and autologging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or REST endpoint. This tutorial will cover the latest innovations from MLflow 1.12. About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unifie... See all the previous Summit sessions: Connect with us: Website: https://databricks.com Facebook:   / databricksinc   Twitter:   / databricks   LinkedIn:   / databricks   Instagram:   / databricksinc   Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-nam...