
Explore MLOps strategies for scaling computer vision, including Flyte for AI product development, its application at Recogni, and lessons from running AI models at scale for planetary satellite imagery analysis.

Gain hands-on experience using Flyte to declaratively specify infrastructure for fine-tuning language models on your own data, overcoming compute resource challenges.

Explore ML infrastructure's role in leveraging open-source tools. Learn about unifying data processing, experiment tracking, and monitoring in a single platform for efficient ML lifecycle management.

Discover how Kubernetes supports AI development with features like autoscaling, GPU management, and ML pipeline orchestration using Flyte for efficient model deployment and serving.