09-22, 16:40–17:10 (US/Eastern), Main Room
In this talk we'll see how to easily run your code at scale through Docker and AWS Batch. We'll cover how to start scaling your workloads once the laptop isn't enough and how open-source can help you achieve that. ML involves training at scale to get the best performance out of the model, and at times it requires heavy-duty GPUs, which requires infrastructure work, security, permissions and operations. We'll cover the steps to deploy it without the Ops, letting you as a data scientist to focus on the important task - getting the most out of the models!
Participants in this talk will be able to:
1. Scale their modeling work.
2. Run their workflows on the cloud.
3. Learn how to build without dealing with infrastructure.
Previous knowledge expected
Ido Michael co-founded Ploomber to help data scientists build faster. He'd been working at AWS leading data engineering/science teams. Single-handedly he built hundreds of data pipelines during those customer engagements together with his team. He came to NY for his MS at Columbia University. He focused on building Ploomber after he constantly found that projects dedicated about 30% of their time just to refactoring the dev work (prototype) into a production pipeline.
Eduardo Blancas is the Co-Founder and CEO of Ploomber, a Y Combinator-backed company developing tools to bridge the gap between interactive data work and production. Before that, he was a Data Scientist at Fidelity Investments, where he deployed the first customer-facing Machine Learning model for asset management. Eduardo holds an M.S. in Data Science from Columbia University and a B.S. in Mechatronics Engineering from Tecnológico de Monterrey.