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Develop and Deploy ML Projects with Metaflow and Seldon
November 15, 2021 @ 12:00 pm - 2:00 pm EST
Speakers: Ville Tuulos, CEO/Co-Founder and and Oleg Avdeev, Co-Founder, Outerbounds; Clive Cox, CTO / Alejandro Saucedo, Director of ML Engineering and Adrián González Martín, ML Engineer, Seldon; Romain Cledat, Software Engineer, Netflix
Over the past years, a new stack of mature, open-source tools for MLOps has started emerging: Metaflow was started at Netflix to make it easy for data scientists to develop robust ML workflows which can be used to train models at scale. Seldon is powering tens of thousands of clusters for model deployments. Together, Seldon and Metaflow cover the full ML project lifecycle from prototyping to production deployments. This is a technical workshop focusing on the full stack of ML infrastructure. Learn from core developers of Seldon and Metaflow how to develop models at scale, and how to deploy them to production-grade infrastructure. We will have an open QA, so come prepared to ask questions about any aspects of ML infrastructure.
What You’ll Learn:
– How to set up infrastructure using open-source tools that allows data scientists to develop models at scale and deploy them as microservices.
– How to leverage Kubernetes clusters for ML and data science effectively.
– Gotchas and lessons learned from thousands of ML projects.
Ville has been developing infrastructure for machine learning for more than two decades. He has worked as an ML researcher in academia and as an infrastructure leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company that continues the Metaflow journey. He is also the author of a new book, Effective Data Science Infrastructure, which will be published by Manning in 2021.
Clive is CTO of Seldon. Seldon helps enterprises put machine learning into production. Clive developed Seldon’s open source Kubernetes based machine learning deployment platform Seldon Core. He is also a core contributor to the Kubeflow and KFServing projects.
Alejandro is the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. Alejandro is also the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning explainability, adversarial robustness and differential privacy. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers.
Romain currently works in the Machine Learning Platform team at Netflix. In that role, he helps data-scientists and other users of machine learning to develop infrastructure that enables them to focus more on data-science, less on infrastructure and provide more business value. Romain is a core developer of Metaflow, the open-source project that originated at Netflix to make data-scientists more productive. Prior to working at Netflix, Romain was at Facebook where he worked on Applied Machine Learning focusing on low-level communication primitives.. Romain graduated with a PhD in Computer Science from the Georgia Institute of Technology where his thesis focused on novel parallelism methods.