MLOps with Flyte - Challenges and Solutions in Machine Learning Operations

Explore MLOps challenges and solutions in this 25-minute CNCF conference talk. Discover how Flyte, an open-source orchestration tool, addresses key issues in machine learning model development and deployment. Learn about reproducibility, recoverability, maintainability, audibility, scalability, and compute management in ML workflows. Gain insights into Flyte's ML-specific features and best practices for fast model development and effective productionization of ML code. Dive into topics such as software engineering practices, technical debt, incremental development, memoization, collaboration, and organizational scaling. Understand Flyte's building blocks, including tasks and workflows, and explore its architecture and features like intra-task checkpointing. Acquire valuable knowledge to enhance your MLOps processes and streamline machine learning projects.

Intro
Software Engineering Practices
Technical Debt
Develop Incrementally and Constantly Iterate @ Scale
Memoize, Recover and Reproduce
Collaboration & Organizational Scaling
Extend Simply
Building Blocks: Tasks
Workflows
Workflow Modalities
#1: Execute and Interact
Features for Platform Folks
Architecture Overview
#1: Intra-task Checkpointing
MLOps Best Practices