Distributed Machine Learning Courses and Certifications

HOTI - Hot Interconnects Symposium

ASTRA-sim and Chakra - Co-design Exploration for Distributed Machine Learning Platforms

Explore co-design methodologies for distributed ML platforms using ASTRA-sim and Chakra frameworks, focusing on system optimization and performance analysis.

• YouTube
• 1 hour 58 minutes
• On-Demand
• Free Video

USENIX

Conspirator - SmartNIC-Aided Control Plane for Distributed ML Workloads

Innovative control plane design using SmartNICs to optimize distributed ML workloads, reducing bottlenecks and improving resource utilization for enhanced performance and efficiency.

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

Introduction to Distributed ML Workloads with Ray on Kubernetes

Learn to scale ML workloads using Ray and Kubernetes. Explore basic Ray concepts, set up a Ray cluster in Kubernetes, and run distributed ML training jobs efficiently.

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

Anyscale

Overcoming Distributed ML Challenges with Ray Train

Unlock efficient distributed ML pipelines with Ray Train. Explore solutions for setup, integration, debugging, fault tolerance, and data processing challenges in scalable machine learning workflows.

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

Data Council

From Scaling to Observability - Solving Key Challenges for Distributed ML with Ray

Explore the observability challenges in distributed ML training with Ray as Nikita Vemuri shares insights from scaling across thousands of nodes and designing effective monitoring systems that balance data visibility with privacy.

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

Devoxx

Distributed Machine Learning 101 Using Apache Spark from a Browser

Explore introductory machine learning concepts and their application at scale using Apache Spark, with hands-on examples in interactive notebooks for real-world distributed data processing.

• YouTube
• 2 hours 39 minutes
• On-Demand
• Conference Talk

USENIX

Scaling Distributed Machine Learning with the Parameter Server

Explore a framework for distributed machine learning using parameter servers, enabling scalable processing of petabyte-scale data with billions of examples and parameters across various algorithms.

• YouTube
• 24 minutes
• On-Demand
• Conference Talk

USENIX

KungFu - Making Training in Distributed Machine Learning Adaptive

KungFu: adaptive distributed machine learning library for TensorFlow. Enables real-time parameter tuning using high-level policies, efficient monitoring, and asynchronous communication for improved training performance.

• YouTube
• 20 minutes
• On-Demand
• Conference Talk

Modern Distributed Systems

Explore essential concepts and challenges in distributed systems, from cloud computing to big data processing. Learn industry-proven solutions for designing scalable, resilient applications in modern IT infrastructure.

• edX
• 6 weeks, 3-5 hours a week
• On-Demand
• Free Online Course (Audit)

MIT HAN Lab

Distributed Training for Efficient Machine Learning - Part I - Lecture 17

Explore distributed training techniques for machine learning, focusing on data and model parallelism strategies to enhance efficiency and scalability.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Docker

Repainting the Past with Distributed Machine Learning and Docker

Explore cutting-edge machine learning techniques to colorize black and white videos using distributed computing with Docker, revolutionizing historical footage preservation at reduced costs.

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

Data Science Festival

Accelerating Machine Learning Serving with Distributed Caches

Explore distributed caching strategies to optimize ML serving, reduce latency, and improve throughput in inference pipelines. Gain practical insights for implementation.

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

MIT HAN Lab

Distributed Training for Efficient Machine Learning - Part II - Lecture 18

Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale systems.

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

Toronto Machine Learning Series (TMLS)

Deploying Distributed AI and Machine Learning in Financial Services

Discover how financial services leverage AI and ML in big data environments to overcome deployment challenges and deliver transformational business results.

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

Databricks

Spark RAPIDS ML - GPU Accelerated Distributed Machine Learning in Spark Clusters

GPU-accelerated machine learning in Spark using RAPIDS ML, offering significant speedup and cost savings over traditional MLlib implementations for large-scale distributed ML applications.

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