The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging. The machine learning lifecycle consists of many complex components, such as data ingestion and prep, model training, tuning, deployment, monitoring and much more. It also requires collaboration and hand-offs across teams, from Data Engineering to Data Science to ML Engineering. Naturally, there is a need for operational rigour to keep all these processes synchronous and working seamlessly. These goals are hard to accomplish without a solid framework to follow.
Machine Learning Operations (MLOps) provides enterprises a framework to successfully deploy AI/ML capabilities into production at any scale. This…
