How Sainsbury's is Bridging the Gap from Model Creation to Production

Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. Join Rico, a Machine Learning Engineer from Sainsburys, as he talks through how Metaflow provides a unified API to the infrastructure stack that is required to execute data science projects, from prototype to production.

Key Takeaways:

• Metaflow allows data science teams to take a workflow from creation to the cloud to production within hours instead of days or even weeks.

• It uses an internal DAG (directed acyclic graph) structure to orchestrate workflows; these can be turned into AWS Step Functions with just one command.

• It solves a big problem for companies who are looking to bridge the gap between data science and engineering.

Rico Meinl, Machine Learning Engineer at Sainsbury's

Enterprising, extroverted Data Scientist with a passion for Artificial Intelligence (AI)/Machine Learning (ML) and a unique ability to easily forge relationships with colleagues, customers, and other business partners. I help data science teams deliver value and ship models with measurable results for the business. At Sainsbury's Tech, I am responsible for building Data Science and Machine Learning models and engineering them to run in production using AWS.

Cookies help us deliver our services. By using our services, you agree to our use of cookies. Learn more