Anyscale Courses

Ray Train: A Production-Ready Library for Distributed Deep Learning

Explore Ray Train's architecture for efficient, cost-effective distributed deep learning. Learn about resource scheduling, API simplicity, and exclusive features for LLM training, including Distributed Checkpointing.

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

Redesigning Scheduling in Ray to Improve Cost-Efficiency at Scale

Explore Ray scheduling features to optimize AI applications, enhancing performance and cost-efficiency. Learn placement groups, graceful node draining, and label-based scheduling for faster, cheaper operations.

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

Ray Scalability Deep Dive: The Journey to Support 4,000 Nodes

Explore Ray's scalability improvements post-2.0, including health checks and resource broadcasting. Learn to develop scalable code for large-scale ML workloads and understand challenges in building a 4000-node cluster.

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

Scaling Time-Series Forecasting Models to Cope with the Multi-Verse with Ray

Learn to scale probabilistic time-series forecasting using Ray for financial markets. Explore techniques for handling non-stationarity and improving model robustness through back-testing and distributed computing.

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

Developing and Serving RAG-Based LLM Applications in Production

Learn to develop, evaluate, and scale RAG-based LLM applications for production, including advanced topics like hybrid routing to bridge the gap between open-source and closed LLMs.

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

AI Factory: Accelerating Solutions with Ray

Explore Lockheed Martin's AI Factory ecosystem for training, deploying, and sustaining AI solutions. Learn how Ray enhances ML workloads, enables distributed computing, and integrates with various tools to accelerate AI development.

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

Deploying Many Models Efficiently with Ray Serve

Explore efficient deployment of multiple models using Ray Serve's features: model composition, multi-application, and model multiplexing. Learn industry patterns and case studies for optimizing resource utilization.

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

From Spark to Ray: An Exabyte-Scale Production Migration Case Study

Explore Amazon's exabyte-scale migration from Spark to Ray, covering challenges, strategies, and future vision for integrating Ray into critical data pipelines.

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

Modernizing DoorDash Model Serving Platform with Ray Serve

Explore DoorDash's journey in modernizing their model serving platform using Ray Serve, focusing on flexibility and self-service for diverse ML applications and frameworks.

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

Scaling up Terascale Deep Learning on Commodity CPUs with ThirdAI and Ray

Explore ThirdAI's BOLT engine for efficient deep learning on CPUs, leveraging Ray Core for distributed training. Learn about overcoming communication bottlenecks and achieving near-linear scaling for terabyte-scale datasets.

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

Lessons From Fine-Tuning Llama-2

Explore insights from fine-tuning Llama-2 for specialized applications. Learn setup, infrastructure, and efficiency techniques to outperform GPT-4 in specific scenarios using Anyscale and Ray.

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

Ray Observability 2.0 - Debugging Applications with New Tooling

Explore new Ray observability tools for debugging ML workloads. Learn to effectively troubleshoot offline and online applications using advanced functionality in Ray and Anyscale.

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

How Spotify Built a Robust Ray Platform with a Frictionless Developer Experience

Discover Spotify's journey in building a centralized Ray platform, enhancing reliability, scalability, and developer experience for diverse ML applications and thousands of users.

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

Scaling AI Health Assistants: Challenges and Solutions

Explore challenges and solutions in scaling AI health assistants, focusing on model management, state persistence, and system reliability. Learn key principles for building scalable AI products using Ray.

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

Forecasting Covid Infections for the UK's NHS Using Ray and Kubernetes

Discover how Ray and Kubernetes were used to forecast COVID-19 infections for the UK's NHS, overcoming scaling challenges and creating a stable architecture for Bayesian modeling.

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