Anyscale Courses

Scalable Feature Engineering with Hamilton on Ray

Explore scalable feature engineering using Hamilton and Ray at StitchFix. Learn declarative dataflow, team scaling, and efficient computation for thousands of time-series features.

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

What's New in RLlib 2.0 - Key Improvements and Features

Explore RLlib 2.0's improvements: configuration, algorithms, customization, policy serving, and Ray AIR integration for intuitive and performant reinforcement learning.

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

Distributed Training with Ray on Kubernetes - Lyft's ML Platform

Leverage Ray on Kubernetes for distributed training at scale. Learn to create on-demand clusters and simplify ML workflows with custom SDKs.

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

Zero-Copy Model Loading with Ray and PyTorch for Efficient Deep Learning Inference

Optimize deep learning model loading for production using PyTorch and Ray. Implement zero-copy techniques to reduce costs and improve performance in NLP model deployments.

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

Hugging Face and Ray AIR Integration for Scalable Machine Learning

Explore Hugging Face and Ray AIR integration for scalable model training and data loading. Learn to create end-to-end workflows from data ingest to inference and serving.

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

Reducing Cost, Latency, and Manual Efforts in Hyperparameter Tuning at Redicell

Optimize machine learning model training with Ray Tune. Reduce costs, latency, and manual efforts through efficient hyperparameter tuning, resource management, and experiment tracking.

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

Introducing Ray AI Runtime: Open-Source Toolkit for Scalable ML Applications

Explore Ray AI Runtime, an open-source toolkit for scalable machine learning applications, featuring functionality, features, and performance benchmarks.

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

Running a Question-Answering System on Ray Serve at Deepset

Learn to deploy a GPU-powered question-answering system using Ray Serve, covering key components, NLP model pipelines, and integration with Hugging Face and persistent storage.

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

Ray Observability - Present and Future

Explore Ray 2.0's observability architecture, learn debugging techniques, and discover future plans for a unified data model in this insightful presentation.

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

Scaling Instacart Fulfillment ML on Ray

Explore scaling Instacart's fulfillment ML using Ray, reducing training time from days to hours. Learn about serverless architecture, remote code execution, and ML Ops integration for efficient model deployment.

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

Scalable Training of Language Models Using Ray, JAX, and TPUv4

Explore challenges and design decisions for scalable language model training, with quantitative analysis of efficiency improvements using Ray, JAX pjit, and TPUv4.

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

Predibase: A Low-Code Deep Learning Platform Built for Scale

Explore Predibase: a low-code deep learning platform combining large-scale ML with state-of-the-art architectures for NLP, computer vision, and tabular data, built on Ludwig and Ray for scalable, end-to-end solutions.

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

The Magic of Merlin - Shopify's New Machine Learning Platform

Explore Shopify's Merlin ML platform, its open-source stack, architecture, and how it scales ML work using Ray.

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

Developing Ray Applications on Google Cloud TPUs

Learn to develop performant, large-scale ML applications using Ray on Google Cloud TPUs, increasing productivity for specialized machine learning workloads.

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

Tend and Tune Your MARL Experiments with Ray and Weights & Biases

Optimize multi-agent reinforcement learning experiments using Ray and Weights & Biases. Automate tuning, trace experiments, and centralize data for faster, more efficient results in scenarios like autonomous driving and drone flying.

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