DevOps to MLOps: Scaling ML Models to 2 Million+ Requests per Day

Explore the journey from DevOps to MLOps in this conference talk that delves into scaling machine learning models to handle over 2 million requests per day. Learn about the key steps in MLOps, including model development, deployment, and monitoring. Discover a real-world case study on eKYC SaaS APIs, examining the cloud-agnostic architecture and scaling strategies employed. Gain insights into eliminating single points of failure, capacity planning, and cost optimization through autoscaling. Analyze production issues related to GPU utilization and high latency, and extract valuable lessons for implementing MLOps at scale. Enhance your understanding of the challenges and solutions in transitioning from traditional DevOps to MLOps for large-scale machine learning applications.