Jim Dowling

ClaudeCode/Codex/OpenCode are general purpose coding agents for software engineering. However, there are also special purpose coding agents for data engineering, known as "Data Agents". Which one should you choose? This talk advocates for general purpose coding agents, as it is a re-run of...

Agents need timely and relevant context data to work effectively in an interactive environment. If an agent takes more than a few seconds to react to an action in a client applicatoin, users will not perceive it as intelligent - just laggy.

LLMs are now capable of translating user queries into small Python programs. In this talk, we will look at how we create AI systems from text with LLMs and Hopsworks.

Retrieval Augmented Generation (RAG) for large language models (LLMs) enables you include real-time information and information created after the cutoff training time for the LLM.

We will build an operational ML system to predict air quality in London. Instead of a single monolithic ML pipeline, we will build a more manageable system as 3 FTI pipelines: a Feature pipeline, a Training pipeline, and an Inference pipeline, connected together by a feature store.

Have you ever trained an awesome model just to have it break in production because of a null value? At its core a feature store needs to provide reliable features to data scientists to build and productionize models. So how can we avoid garbage in, garbage out situations? Great expectations is...