Build Scalable and Viable Machine Learning Infrastructure for Product Success

In today's data-driven world, machine learning (ML) has become an essential tool for businesses to gain insights and build innovative products. However, successfully deploying and managing ML models at scale requires a robust and scalable infrastructure. This session will explore the key considerations for building and maintaining an ML infrastructure that supports product development and growth. We will discuss topics such as model training and deployment pipelines, data management, monitoring, and scalability. Additionally, we will share best practices for collaboration between product managers and ML engineers to ensure alignment and success.