MLOps Community

With the open source releasing foundational models at a blistering pace there has never been a better time to develop an AI-powered product. In this talk, we walk you through the challenges and state-of-the-art techniques that can help you fine-tune your own LLMs. Additionally, we will provide guidance on how to determine when a small model would be more appropriate for your use case.

MLOps is particularly challenging to implement in enterprise organizations due to the complexity of the data ecosystem, the need for collaboration across multiple teams, and the lack of standardization in ML tooling and infrastructure. In addition to these challenges, at Ahold Delhaize, there is a requirement for the reusability of models as our brands seek to have similar data science products, such as personalized offers, demand forecasts, and cross-sell.

Simba Khadder shares his insights during a talk at the MLOps Meetup in San Francisco, highlighting their experience with 100 million monthly active users. They discussed their failures and learnings, primarily focusing on building a recommended system. By utilizing embeddings, they generated candidate song recommendations and refined the rankings. Simba emphasized the significance of resolving workflow challenges to enhance the effectiveness of data scientists. They introduced the concept of a feature store, which revolutionized their approach to data science.