Develop Incrementally and Constantly Iterate @ Scale
5
Memoize, Recover and Reproduce
6
Collaboration & Organizational Scaling
7
Extend Simply
8
Building Blocks: Tasks
9
Workflows
10
Workflow Modalities
11
#1: Execute and Interact
12
Features for Platform Folks
13
Architecture Overview
14
#1: Intra-task Checkpointing
15
MLOps Best Practices
Description:
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.
MLOps with Flyte - Challenges and Solutions in Machine Learning Operations