AI Inference Workloads - Solving MLOps Challenges in Production
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the challenges and solutions for AI inference workloads in production environments during this 55-minute conference talk from the Toronto Machine Learning Series. Dive into the complexities of moving machine learning prototypes to production, focusing on throughput, latency, and GPU utilization. Learn about fractional GPU capabilities and their impact on performance. Discover how a leading organization built an inference platform using Kubernetes and NVIDIA A100 MIG technology to scale AI initiatives. Gain insights into deployment types for inference workloads, embedding ML models into web servers, and decoupling web and model serving. Understand the concept of Multi-Instance GPU (MIG) and its applications in model inferencing. Benefit from the speaker's expertise in DevOps, Cloud Computing, Kubernetes, and AI computing to overcome MLOps challenges and optimize your AI inference workflows.