How GitOps approach can save millions for large tech firms — Revolutionising ML operations with Amreth Chandrasehar

Artificial Intelligence (AI) is no longer just science fiction, it has now become a part of our daily lives. From voice assistants to personalised recommendations on streaming platforms, AI is all around us. But have you ever wondered how these AI systems work behind the scenes? Well, that's where machine learning operations, or MLOps, comes into play. Think of MLOps as the conductor of an orchestra, making sure all the AI models and algorithms work harmoniously to deliver the best possible results.

In the world of tech giants, the stakes are higher. These companies operate on a grand scale, handling vast amounts of data and serving millions, if not billions of users globally. Ensuring the seamless operation of their AI systems is not just a matter of convenience, but one of survival. This is where the GitOps approach emerges as a game-changer, revolutionising ML operations and potentially saving these tech giants millions of dollars.

Meet the expert, Amreth Chandrasehar:

To help us navigate this landscape, we turn to Amreth Chandrasehar, a seasoned expert in AI and technology. Chandrasehar has been at the forefront of this digital revolution, working with big names like T-Mobile and Amazon. He's the wizard behind the curtains, making sure the AI machinery hums along smoothly. Currently serving as director of ML engineering, observability, and site reliability engineering at Informatica, he shares his insights and illustrates how GitOps is transforming the way large tech companies handle their AI operations.

Understanding the Complexity of MLOps for Tech Giants

Large tech firms operate in a complex environment where AI systems are the driving force behind their services. These systems continuously evolve, requiring constant attention at every stage of their lifecycle, from development and training to deployment and real-time monitoring.

“Imagine managing a high-speed train hurtling down a track at full throttle. You need to keep it on course, ensure it runs efficiently, and prevent any unexpected derailments. Now, multiply this by the millions of users relying on your services. That's the challenge tech giants face daily in the world of AI," says Chandrasehar.

The GitOps Approach: A Revolution in MLOps

GitOps is the cavalry that arrives to simplify and streamline this daunting task. It's a set of practices and tools that automates the entire process of managing AI systems, from coding and development to deployment and monitoring.

At its core, GitOps is about leveraging the power of version control, typically associated with software development, to manage the full machine learning lifecycle. It provides a single source of truth for AI systems, ensuring that every change is documented, approved through pull request processes, and subjected to automated quality checks.

The GitOps Pipeline: Efficiency and Automation

Chandrasehar highlighted that GitOps employs a sophisticated pipeline, comprising two vital components: Continuous Integration (CI) and Continuous Deployment (CD).

The CI phase encompasses pre-commit and post-commit pipelines. In the pre-commit phase, the system checks whether quality metrics are met. In the post-commit phase, end-to-end test cases are executed, and if quality metrics are met, the artifact is pushed to the model registry.

— Shagun Sharma is a business journalist.