We like talking about production – one famous, but probably wrong statement about it is “87% of data science projects never make it to production”.
While giving a talk to a group of up-and-coming data scientists, a question that surprised me came up:
"When you say “production”, what exactly do you mean?"
Buzzwords are great, but all the cool kids know what production is, right? Wrong.
In this talk, we’ll define what production actually means. I’ll present a first-principles, step-by-step approach to thinking about deploying a model to production. We’ll talk about challenges you might face in each step, and provide further reading if you want to dive deeper into each one.
Dean has a background combining physics and computer science. He’s worked on quantum optics and communication, computer vision, software development and design. He’s currently CEO at DagsHub, where he builds products that enable data scientists to work together and get their models to production, using popular open source tools. He’s also the host of the MLOps Podcast, where he speaks with industry experts about ML in production.