True ML Talks #2 | Machine Learning Workflow @ Stitch Fix

True ML Talks #2 | Machine Learning Workflow @ Stitch Fix

TrueMLTalks is a video series in which we will host ML leaders and delve into the ML stacks of their organizations. We'll get a quick primer on their experience in managing ML pipelines and building best practices along the way. In this series, we are speaking with ML-first organizations, including Gong, StichFix, SalesForce, Facebook, Simpl, and many more.
Stefan Krawczyk, CEO of DRAGWorks & ex-Model Lifecycle team at Stitch Fix, will be joining us for our next talk in the series.

In this video, Stefan shares his expertise and insights on:
✅Machine Learning use cases at Stitch Fix.
✅How Stitch Fix's team is structured to optimize the business outcomes.
✅Challenges faced in the build-out of ML stack with specific challenges that come pertaining to the industry.
✅An overview of cutting-edge innovations applied during the process of building and scaling ML infrastructure.

Don't miss this opportunity to learn from one of the brightest minds in the ML industry. Tune in on the next episode of TrueMLTalks now!

About Our Guest :
Stefan is building DAGWorks, a collaborative open-source platform for data science teams to build and maintain model pipelines, plugging into existing MLOps and data infrastructure (read their YC Launch). He has over 15 years of experience at companies such as Nextdoor, LinkedIn, and Stitch Fix in the field of data and machine learning. He previously led the Model Lifecycle team at Stitch Fix, where he gained extensive experience building self-service tooling for an internal MLOps machine learning platform. He's also a regular conference speaker and author of the popular open-source framework, Hamilton.

⚡Reach out to him -

⚡Know more about DRAGWorks:

About TrueFoundry:
TrueFoundry is a cross-cloud ML deployment PaaS that helps organizations to accelerate developer workflows for model testing and deployment while maintaining full security and control for the Infra/DevSecOps team. We enable Machine Learning Teams to deploy and monitor models in 15 minutes with 100% reliability and scalability, allowing them to save money and release Models to production faster, enabling actual business value to be realized. We deploy on the customer's infrastructure, ensuring data privacy and other security concerns are addressed.

Timestamps
00:00 - start
02:09 - Introduction by Stefan
06:21 - Team organization setup within StitchFix
13:27 - How was the infrastructure allocation handled?
14:54 - Inovations and solutions that stood out
19:12 - Replacing certain parts from the Docker container
24:32 - Aspects of challenges that were unique to StitchFix
26:37 - Using open-source tools that solve these problems or building tools in-house.
30:31- Different impacts of machine learning that are not intuitive to external users
34:20 - Combination of tools that helps build a fairly mature MLOps pipeline
39:50 - Next chapter of achievements through DAGWorks and the product that Stefan is trying to build
44:22 - How is Hamilton similar to or different from Metaflow?

#mlops #machinelearning #artificialintelligence