Module 1: Why Ray and Why Anyscale

Module 2: Anyscale Overview

Understanding Anyscale Workspaces and compute resources
- Workspaces: cluster and nodes, configuration management
- Developer environment: terminal, VSCode, JupyterLab, remote development
- Managing dependencies and environment variables
Monitoring and debugging Ray applications
- Ray Dashboards, grafana metrics
- Unified Logs Viewer
Configurations
- Container images
- Compute configurations
Production Jobs
- Jobs, Job Queues and Job Schedules
- Alerts, configurations: retries, priorities, example yaml
- dev-to-prod experience: Running jobs from the workspace
- prod-to-dev experience: duplicate job as a workspace
Production Services
- Configurations
- HA Clusters
- Ray Serve Integration

Module 1: Introduction to the Ray AI Libraries

Module 2: Introduction to Ray Train

Module 3: Introduction to Ray Tune

Module 4: Introduction to Ray Data

Module 5: Introduction to Ray Serve

Module 6: Introduction to Ray Core