Anyscale Courses

Large-Scale Data Shuffle in Ray with Exoshuffle

Explore high-performance large-scale data shuffling with Exoshuffle in Ray, outperforming Spark and achieving near-theoretical performance for ML applications.

• YouTube
• 26 minutes
• On-Demand
• Free Video

How Spotify Sped Up ML Research and Prototyping with Ray

Accelerate ML research and prototyping using Ray's distributed computing platform. Explore Spotify's journey, infrastructure integration, and best practices for enhanced model development and experimentation.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Gearing Up for FIFA 2022 Using RLlib-Powered Traffic Control

Optimize traffic control for FIFA 2022 using RLlib-powered multi-agent reinforcement learning, microsimulation, and Graph Convolutional Networks for congestion management in Qatar.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Ray Summit 2022 - Robotics and AI: Challenges and Opportunities - Day 2 Keynote

Explore cutting-edge AI research and applications with Anca Dragan's keynote address at Ray Summit 2022, focusing on advancements in machine learning and distributed computing.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Accelerating Scientific Discovery with AI and Quantum Computing

Explore cutting-edge insights on distributed computing and AI from IBM's Dario Gil in this Ray Summit 2022 keynote address.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Ray Workflow - Durable Ray Tasks for Efficient, Flexible Data Pipelines

Explore efficient, flexible data pipelines using Ray Workflow. Learn to implement durable Ray tasks for ETL workloads and ML pipelines, combining workflow system advantages with Ray's data processing capabilities.

• YouTube
• 37 minutes
• On-Demand
• Free Video

State of Ray Serve in 2.0 - Features and Updates for Multi-model Inference

Explore Ray Serve 2.0's features, use cases, and architecture for multi-model inference and composition. Learn about autoscaling and production hardening techniques.

• YouTube
• 32 minutes
• On-Demand
• Free Video

Alpa: Simple Large Model Training and Inference on Ray

Explore Alpa, a Ray-native library for automated model-parallel training of large deep learning models, optimizing execution plans and scaling complex architectures efficiently.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Highly Available Architectures for Online Serving in Ray

Explore highly available serving in Ray 2.0, its architecture, functionality, and practical deployment for improved efficiency and reduced disruption during failures.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Ray for Distributed Mixed Integer Optimization at Dow

Optimize complex production schedules using multi-agent decomposition and Ray for faster decision-making in mixed integer linear programming problems.

• YouTube
• 29 minutes
• On-Demand
• Free Video

KubeRay - A Kubernetes Ray Clustering Solution

Explore KubeRay, an open-source toolkit for managing Ray clusters on Kubernetes. Learn about architectural decisions, resource management, job submission, and autoscaling capabilities.

• YouTube
• 30 minutes
• On-Demand
• Free Video

FastCFE - A Counterfactual Explainer Using Reinforcement Learning

Explore reinforcement learning for real-time counterfactual explanations in machine learning explainability. Learn about FastCFE algorithm, OpenAI Gym, and Ray+RLlib for actionable insights in various applications.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Wind Farm Optimization Using Reinforcement Learning and Large-Scale Simulation

Optimize wind farm energy production using deep reinforcement learning and large-scale simulations. Boost annual output by 1-2% through advanced yaw control strategies.

• YouTube
• 28 minutes
• On-Demand
• Free Video

A Serverless Resilient Graph Analysis Platform Built on Ray - ByteGAP

Explore ByteGAP: a serverless graph analysis platform for processing super large-scale graphs, built on Ray at ByteDance. Learn about its architecture, features, and advantages.

• YouTube
• 34 minutes
• On-Demand
• Free Video

Multi-Model Composition with Ray Serve Deployment Graphs

Leverage Ray's distributed computing for authoring, orchestrating, scaling, and deploying complex serving graphs as a DAG under one set of APIs.

• YouTube
• 30 minutes
• On-Demand
• Free Video