Ville Tuulos

Ville Tuulos

Co-founder and CEO at Outerbounds

    Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for ML infrastructure. He is the co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of the book, Effective Data Science Infrastructure, published by Manning.

    All Sessions by Ville Tuulos

    Day 2 04/24/2024
    11:35 am - 12:05 pm

    Beyond MLOps: Building AI systems with Metaflow

    <span class="etn-schedule-location"> <span class="firstfocus">MLOps </span> </span>

    Open-source Metaflow has been powering ML systems at companies like Netflix, 23andMe, and Goldman Sachs for years. With the advent of Generative AI, companies are starting to consider how the new techniques can be embedded in existing applications, and how they can power wholly new product experiences. This requires new engineering and infrastructure, in particular if the company wants to own the models and the user experience, integrating AI tightly into their business and systems, going beyond widely available commercial APIs. In this talk, we will provide an overview of how Metaflow helps you build novel, differentiated AI-powered systems that require large-scale data engineering, model training, content embedding, inference, and more. We will cover changes compared to earlier ML stacks, focusing on the quickly growing compute needs in particular, and share our recent experiences from real-life large-scale AI use cases, and how they interoperate with existing data and ML systems.

    2:00 pm - 2:30 pm

    Building production-grade ML/AI Systems with Outerbounds Platform

    Building real-life, production-grade ML/AI systems is not easy. To build systems powered by RAG, custom LLMs, other GenAI patterns, or systems powered by classic predictive ML, you need access to data, compute, orchestration, versioning, modeling, and deployment tools. And you need these easily accessible for data scientists and ML Engineers, all while making sure your platform and infrastructure engineers have their needs met. In this demo, we will provide an overview of how to build novel, differentiated AI-powered systems that require large-scale data engineering, model training, content embedding, inference, and more, all using open-source Metaflow, originally developed at Netflix, and running on the secure, managed platform provided by Outerbounds.

    Open Data Science




    Open Data Science
    One Broadway
    Cambridge, MA 02142

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