Savin Goyal

Careful management of software dependencies is one of the most underrated parts of ML and AI systems despite being critically important for the stability of production deployments as well as for the speed of development. For the past many years, we have worked with the wider Python package management community (pip, conda, rattler, uv, and many more) and multiple organizations (Netflix, Amazon, Goldman Sachs, and many more) to advance the state of the art in dependency management for ML/AI platforms, including our open-source framework Metaflow.

Airflow is a household brand in data engineering: It is readily familiar to most data engineers, quick to set up, and, as proven by millions of data pipelines powered by it since 2014, it can keep DAGs running.

One of the key questions in modern data science and machine learning, for businesses and practitioners alike, is how do you move machine learning projects from prototype and experiment to production as a repeatable process. In this workshop, we present an introduction to the landscape of production-grade tools, techniques, and workflows that bridge the gap between laptop data science and...

There is a pressing need for tools and workflows that meet data scientists where they are. This is also a serious business need: How to enable an organization of data scientists, who are not software engineers by training, to build and deploy end-to-end machine learning workflows and applications...

Data science at Media Solutions goes far beyond eponymous recommendation systems and touches every aspect of the business, from optimizing content delivery to fighting fraud. Media Solutions’s unique culture affords its data scientists extraordinary freedom of choice in tools, which results in an ever-expanding set of machine learning (ML) approaches and systems.