Distributed Machine Learning Courses and Certifications

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 Online Course

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)

UC Berkeley Microcredential

Data Science and Engineering with Spark

Learn to perform data science and engineering at scale using Apache Spark. Gain hands-on experience with big data analysis, distributed machine learning, and Spark architecture through practical applications.

• edX
• 10 weeks, 5-10 hours a week
• On-Demand
• $247.00

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

USENIX

Oort - Efficient Federated Learning via Guided Participant Selection

Efficient federated learning system that improves model training and testing by intelligently selecting participants based on data utility and device capabilities, enhancing accuracy and performance.

• YouTube
• 16 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

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

Accelerating Distributed MoE Training and Inference with Lina

Explore Lina, an innovative system for accelerating distributed Mixture of Experts training and inference, addressing all-to-all communication bottlenecks in large-scale AI models.

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

Stream Processing and New Approaches: Edge Processing

Explore stream processing on the edge for real-time data analysis, enabling rapid anomaly detection and distributed machine learning using open-source software.

• YouTube
• 40 minutes
• On-Demand
• Free Online Course

IoT and Stream Processing - Real-Time Data Analysis on the Edge

Explore IoT data analysis through stream processing on edge devices, enabling real-time insights and machine learning in embedded Linux environments using open-source solutions.

• YouTube
• 45 minutes
• On-Demand
• Free Online Course

TheIACR

Multi-party Computation I - Session 109

Explore the fundamentals and applications of Multi-party Computation, a cryptographic protocol enabling secure collaborative computations while preserving data privacy.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Online Course

TheIACR

PPML 2020 - Welcome and Invited Talk - Contributed Talks

Explore cutting-edge research in privacy-preserving machine learning, including split learning, secure inference, fairness certification, crypto-oriented neural architectures, and privacy-preserving decision trees.

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

USENIX

Learning and Unlearning Your Data in Federated Settings

Explore federated learning and unlearning approaches for collaborative AI model training while preserving data privacy. Analyze methods for removing data inputs in distributed settings to comply with regulations.

• YouTube
• 15 minutes
• On-Demand
• Free Online Course

IEEE

SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks

Explore robust algorithms for privacy-preserved federated learning in future networks, focusing on explainable and secure data collaboration techniques.

• YouTube
• 15 minutes
• On-Demand
• Free Online Course

CNCF [Cloud Native Computing Foundation]

Accelerate Your GenAI Model Inference with Ray and Kubernetes

Explore serving large GenAI models efficiently using KubeRay on Kubernetes with hardware accelerators. Learn to scale workloads, optimize performance, and reduce costs for production-ready AI inference.

• YouTube
• 28 minutes
• On-Demand
• Free Online Course