Machine learning (ML) and artificial intelligence (AI) are transforming businesses across industries, but implementing these technologies can be challenging. The critical role of MLOps in delivering ROI through improved AI systems is often overlooked. MLOps combines software engineering best practices with ML workflows to create a repeatable, reliable, and scalable process for deploying ML models into production.

In this talk, we will discuss the importance of MLOps in delivering business value by improving AI systems. We will explore the key challenges businesses face in implementing and scaling AI solutions and how MLOps can help overcome these challenges. We will also cover the essential components of a successful MLOps pipeline, including data management, model training and evaluation, and model deployment and monitoring.

The talk will feature real-world examples of companies that have successfully implemented MLOps to deliver business value, including improved accuracy, faster time to market, and increased ROI. We will also discuss the potential risks and pitfalls of MLOps and how to address them.

Attendees will leave the talk with a clear understanding of the business value of MLOps and how to implement an effective MLOps pipeline in their organizations.