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.
– 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 https://www.databricks.com/try-databricks
– Pre-register for Databricks Community Edition
– Basic knowledge of Python programming language
– Basic understanding of machine learning concepts
Sr. Resident Solutions Architect at Databricks with a focus on MLflow and ML production pipelines.
Ricardo Portilla is a Solutions Architect at Databricks specializing in machine learning. As part of this role, he supports companies looking to scale data science operations, optimize machine learning training, and deploy models to production. Previously, he worked as a technical lead and data scientist at FINRA, primarily working with financial time series and big data anomaly detection applications. He completed a mathematics Ph.D at the University of Michigan.