Building Scalable Machine Learning Platforms: A Practical Guide for Engineers

Join me, Yash, as I take you through the key strategies and technologies behind designing and managing large-scale machine learning platforms. Drawing from my 25+ years of experience, including my role as a Lead Software Engineer at JPMorgan Chase, this session will cover how to build robust ML infrastructure, optimize distributed model training, and transition on-premise workloads to cloud-based solutions like AWS SageMaker and EMR. You'll learn about: 1 Best practices for scaling ML models across petabyte-scale data 2 Leveraging cloud services (AWS, EMR, EC2, SageMaker) for efficient ML workflows 3 Implementing governance, compliance, and cost-optimization strategies 4 Case studies on fraud detection and risk assessment using ML