Andre Mesarovic

Sr. Resident Solutions Architect, Databricks

Sr. Resident Solutions Architect at Databricks with a focus on MLflow and ML production pipelines.

Past sessions

Summit 2021 MLflow モデルサービング

May 27, 2021 03:15 PM PT

Discuss the different ways model can be served with MLflow. We will cover both the open source MLflow and Databricks managed MLflow ways to serve models. Will cover the basic differences between batch scoring and real-time scoring. Special emphasis on the new upcoming Databricks production-ready model serving.

In this session watch:
Andre Mesarovic, Sr. Resident Solutions Architect, Databricks

[daisna21-sessions-od]

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.

To solve for these challenges, Databricks unveiled last June MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.

WHAT YOU WILL LEARN:
– Understand the 3 main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
– How to use MLflow Tracking to record and query experiments: code, data, config, and results.
– How to use MLflow Projects packaging format to reproduce runs on any platform.
– How to use MLflow Models general format to send models to diverse deployment tools.

PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Python 3 and pip pre-installed
– Pre-register for a Databricks Standard Trial at http://databricks.com/try
– Pre-register for Databricks Community Edition
– Basic knowledge of Python programming language
– Basic understanding of machine learning concepts

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.

To solve for these challenges, Databricks unveiled last June MLflow, an open source project that aims at simplifying the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.

WHAT YOU WILL LEARN:
– Understand the 3 main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
– How to use MLflow Tracking to record and query experiments: code, data, config, and results.
– How to use MLflow Projects packaging format to reproduce runs on any platform.
– How to use MLflow Models general format to send models to diverse deployment tools.

PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Python 3 and pip pre-installed
– Pre-register for a Databricks Standard Trial at http://databricks.com/try
– Pre-register for Databricks Community Edition
– Basic knowledge of Python programming language
– Basic understanding of machine learning concepts