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Workshop | Managing the Complete Machine Learning Lifecycle with MLflow: 1 of 3

Join us for a 3 part online technical workshop series: Managing the Complete Machine Learning Lifecycle with MLflow. If you’re interested in learning about machine learning and MLflow, this workshop series is for you!

Workshop 1 of 3 | Introduction to MLflow: How to Use MLflow Tracking

Level: Beginner/Intermediate Data Scientist or ML Engineer

Details: This workshop is an introduction to MLflow. Machine Learning (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 these challenges, MLflow (https://mlflow.org/), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, encapsulate models that can be used with many existing tools, and central repository to share models, accelerating the ML lifecycle for organizations of any size.

What you will learn: Understand the four main components of open source MLflow—MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each component helps address challenges of the ML lifecycle.

– How to use MLflow Tracking to record and query experiments: code, data, config, and results. (https://mlflow.org/docs/latest/tracking.html)

– How to use MLflow Projects packaging format to reproduce runs. (https://mlflow.org/docs/latest/projects.html)

– How to use MLflow Models general format to send models to diverse deployment tools. (https://mlflow.org/docs/latest/models.html)

– How to use Model Registry for collaborative model lifecycle management. (https://mlflow.org/docs/latest/model-registry.html)

– How to use MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics. (https://mlflow.org/docs/latest/tracking.html#tracking-ui)

Suggested prep work: To get the most out of this series, please review and complete the prep work here: https://github.com/dmatrix/mlflow-workshop-part-1#prerequisites

Agenda: 9AM PDT – 10AM PDT (GMT-8)

9:00AM – 9:50AM – Workshop led by Jules
9:50AM – 10:00AM – Q&A

To join the live chat, check out the meetup page: https://www.meetup.com/data-ai-online/events/270223876/ 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-named-leader-by-gartner