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 MLflow model serving, talks about scoring models with MLflow (including online with MLflow scoring server and offline with Apache Spark), and custom model deployment and scoring.
## Speaker ##
Andre Mesarovic, Resident Solutions Architect, Databricks
Andre Mesarovic is a Resident Solutions Architect at Databricks with a focus on MLflow, model serving and ML production pipelines. Andre has been working with Spark since 2014.
## More Info ##
databricks.com/blog/2020/06/25/announcing-mlflow-model-serving-on-databricks.html 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. databricks.com/databricks-named-leader-by-gartner
slides: github.com/amesar/mlflow-resources/blob/master/slides/MLflow_Model_Serving_DAIS_2021.pdf
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