In the world of GenAI, advancements are happening at a crazy speed. These advancements concern not only the algorithms but also the operations side of things. In this talk, we will go back to the basics, discuss the main principles of building robust ML systems (traceability, reproducibility, and monitoring), and explain what types of tools are required to support these principles for different types of applications.

At Ahold Delhaize, we have 19 local brands. Most of our brands have common goals, such as providing personalized offers to their customers, a better search engine on e-commerce websites, and forecasting models to reduce food waste and ensure availability. As a central team, our goal is to standardize the way of working across all of these brands, including the deployment of machine learning models. To this end, we have adopted Databricks as our standard platform for our batch inference models.

However, API deployment for real time inference models remained challenging due to the varying capabilities of our brands. Our attempts to standardize API deployments with different tools failed due to complexity of our organization. Fortunately, Databricks has recently introduced a new feature: serverless API deployment. Since all our brands already use Databricks, this feature was easy to adopt. It allows us to easily reuse API deployment across all of our brands, significantly reducing time to market (from 6-12 months to one month), increasing efficiency, and reducing the costs. In this session, you will see the solution architecture, sample use case specifically used to cross-sell model deployed to four different brands, and API deployment using Databricks Serverless API with custom model.

Talk by: Maria Vechtomova and Basak Eskili

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