Explore the latest advancements in MLflow, the widely-used open source platform for managing the full machine learning lifecycle, in this keynote from the Spark + AI Summit 2020. Discover how MLflow simplifies the complex process of standardizing MLOps and productionizing ML models. Learn about new features including simplified experiment tracking, innovations in model format for improved portability, capabilities for managing and comparing model schemas, and enhanced model deployment methods. Gain insights into real-world use cases and understand how MLflow addresses the challenges of reproducibility, agility, and predictability in ML model development and deployment. Delve into topics such as auto-logging, custom tagging, model surfing, and the model registry. Get practical advice on how to leverage these new capabilities to streamline your ML workflows and improve productivity.
Simplifying Model Development and Management with MLflow - New Features and Innovations
Databricks via YouTube
Overview
Syllabus
Introduction
Building ML Applications
Machine Learning Platforms
Use Cases
Virgin Hyperloop
Data Books
Whats Next
Auto Logging
Spark Greenery Sources
MLflow Auto Logging
Database Auto Logging
Model Schemas
Custom Tags
Model Deployment ML
Model Deployment API
Model Surfing
Model Registry
Model Versions
Recap
How to get started
Taught by
Databricks
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Complete ML Lifecycle with MLflow - Learn Its Four Components
