Coffee Sessions #94

Traversing the Data Maturity Spectrum: A Startup Perspective

A lot of companies talk about having ML and being data-driven, but few are there currently and doing it well. If anything, many companies are on the cusp of implementing ML rather than being ML mature. As a startup, what decisions are we making today to drive data maturity and set us up for success when we further implement ML in the near future? What business cases are we making for leadership buy-in to invest in data infrastructure as compared to product development while we identify product-market-fit?

Take-aways

1. Data maturity is a spectrum that takes time to traverse and for companies to ultimately be capable of production ML. 2. Your organizations overall strategy is what determines the speed in which you traverse the data maturity spectrum. 3. Along the data maturity journey a data team has to make various long-term decisions... ensure such decisions don't block being able to implement ML in the future.

In this episode

Mark Freeman

Mark Freeman

Senior Data Scientist, Humu

Mark is a community health advocate turned data scientist interested in the intersection of social impact, business, and technology. His life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized. Mark received his M.S. from the Stanford School of Medicine where he was trained in clinical research, experimental design, and statistics with an emphasis on observational studies. In addition, Mark is also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. He is currently a senior data scientist at Humu where he builds data tools that drive behavior change to make work better. His core responsibilities center around 1) building data products that reach Humu's end users, 2) providing product analytics for the product team, and 3) building data infrastructure and driving data maturity.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Vishnu Rachakonda

Vishnu Rachakonda

Host

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.