APIs: They Matter More Than You Think in Machine Learning
There’s been a great deal of work in ML research during the last decade. What we've seen the last 3 years is a new movement orthogonal to ML, ML Ops. ML Ops is still in its infancy, but its premise is how to create an ML infrastructure that will promote best practices and expedite ML projects. ML practitioners can be seen as chefs; they need the proper tooling to unveil their talent. However, the interface of this tooling is crucial to make them useful to ML practitioners. In this talk, I’ll provide my view on the importance of APIs and interfaces in Machine Learning.
Pavlos Mitsoulis has 10 years of Machine Learning and Software Engineering experience. Currently, he is an Engineering Manager of ML Ops team at King (part of Activision Blizzard), leading King's central ML Platform. Additionally, he is the creator of Sagify, an open-source library that simplifies training, tuning, evaluating, and deploying ML models to SageMaker.