Deploying Machine Learning Models at Scale in Cloud
The way Data Science is done is changing. Notebook sharing and collaboration were messy and there was minimal visibility or QA into the model deployment process. Vishnu will talk about building an ops platform that deploys hundreds of models at-scale every month. A platform that supports typical features of MLOps (CI/CD, Separated QA, Dev and PROD environment, experiments tracking, Isolated retraining, model monitoring in real-time, Automatic Retraining with live data) and ensures quality and observability without compromising the collaborative nature of data science.
In this episode
Vishnu Prathish
Director Of Engineering, AI Products, Innovyze
With 10 years in building production grade data-first software at BBM & HP Labs, I started building Emagin's AI platform about three years ago with the goal of optimizing operations for the water industry. At Innovyze post-acquisition, we are part of the org building world leading water infrastructure data analytics product.
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.