Anyscale Courses

NVIDIA NeMo Curator - Scaling Multi-Modal Data Curation Workflows

Discover how to architect petabyte-scale multi-modal data pipelines for Generative AI using Ray's distributed computing primitives and GPU acceleration patterns.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

How Alibaba Cloud Accelerates AI Pipelines with AnalyticDB Ray

Discover how Alibaba Cloud integrates Ray's distributed computing into AnalyticDB for high-performance multi-modal AI pipelines, boosting GPU usage and accelerating inference workflows.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Unlocking Peak Workload Performance and Efficiency with Ray on Kubernetes

Discover how to build cost-effective ML platforms using Ray on Kubernetes with GKE scheduling, Kueue fair-share, TPU training, and dynamic GPU/TPU workload optimization strategies.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

NVIDIA's Framework for Scalable Data Curation

Discover how NVIDIA and Roblox built a scalable ML platform using Ray, covering architecture, KubeRay integration, and migrating from MPI to Ray Train for foundation model training.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Wisedocs' Journey - Rebuilding and Accelerating ML with KubeRay

Discover how Wisedocs rebuilt their ML serving stack with KubeRay, achieving 50% cost reduction and 10× throughput improvement while cutting deployment time from one month to two days.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Exabyte-Scale Streaming Iceberg IO with Ray, Flink, and DeltaCAT

Discover how to architect exabyte-scale Iceberg workflows using Ray, Flink, and DeltaCAT for high-throughput streaming data operations and scalable table management.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Agentic Workload Inference at Scale - ByteDance's AIBrix and DeerFlow

Discover ByteDance's AIBrix & DeerFlow: open-source infrastructure for scalable LLM inference with smart autoscaling, KVCache management, and agentic workload support.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Pinterest's Approach to Real-Time ML Experimentation Using Ray

Discover how Pinterest transformed ML experimentation from weeks to days using Ray's real-time streaming pipeline, achieving 10x faster model updates and saving hundreds of thousands in costs.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

How Prime Intellect Builds Scalable Infrastructure for Agentic RL

Discover how Prime Intellect architects distributed reinforcement learning infrastructure at scale, featuring async-first trainers, fault-tolerant execution, and multi-cloud compute platforms.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

How Workday Achieved 50x Cheaper Model Serving with Ray Serve

Discover how Workday rebuilt their ML model-serving architecture with Ray Serve, achieving 50x cost savings while scaling to tens of thousands of models across multiple environments.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Scaling LLM Post-Training at Character.AI

Discover how Character.AI scales LLM post-training for millions using Ray ecosystem, Rayman platform, and open-source RL libraries for AI entertainment at global scale.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

MLOps with Ray on Anyscale

Discover how to streamline ML workflows using Ray on Anyscale for scalable data processing, distributed training, hyperparameter tuning, and production deployment.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

How to Get Started with Distributed Training at Scale

Master distributed training strategies for scaling deep learning models using data, model, and pipeline parallelism with PyTorch and Ray to overcome bottlenecks and system failures.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Maximizing Compute Efficiency on Anyscale

Discover advanced observability tools for Ray distributed AI workloads, featuring scalable dashboards, debugging techniques, and the new open-source Ray Export API for better optimization.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

RLlib - Lessons from the V2 Stack and Road Ahead

Discover RLlib v2's architecture redesign for massive distributed reinforcement learning, scaling to 10,000+ environment runners with enhanced reliability and performance insights.

• YouTube
• 32 minutes
• Self-Paced
• Free Video