MLOps holds the key to accelerating the development and deployment of AI, so that enterprises can derive real business value from their AI initiatives. From the first model deployed to scaling data science across the organization. The foundation you set will enable your team to build and monitor a growing amount of AI applications in production. In this talk, we will share best practices from our experience with enterprise customers who have effectively built and deployed composite machine and deep learning pipelines.

Given the growing number of AI projects and the complexities associated with bringing these projects to production, and specifically the challenges associated with feature engineering, the industry needs a way to standardize and automate the core of feature engineering. Feature stores provide enterprises with a competitive edge, as they enable them to expedite and simplify the path from lab to production. They enable sharing and re-use of features across teams and projects to save time and effort and ensure consistency across training and inference.

The data science team at IHS Markit has been hard at work building sophisticated NLP pipelines that work at scale using the Iguazio MLOps platform and open-source MLRun framework. Today they will share their journey and provide advice for other data science teams looking to: Nick (IHS Markit) and Yaron (Iguazio) will share their approach to automating the NLP pipeline end to end. They’ll also provide details on leveraging capabilities such as Spot integration and Serving Graphs to reduce costs and improve the data science process.