Creating an ML model is just a starting point. The challenge is getting the model deployed into a production environment and keeping it operational and supportable. Organizations need to manage the end to end lifecycle of code, data, models and applications and services—a task that spans multiple personas and multiple clouds.
Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.
Sarah Bird leads research and emerging technology strategy for AI developer products in Azure at Microsoft. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations in machine learning and systems research with the best of open source and product expertise to create new tools and technologies. She’s an active contributor to the open source ecosystem and cofounded ONNX, an open source standard for machine learning models. She was also a leader in the PyTorch 1.0 project. Sarah’s research interests include machine learning systems and responsible AI. She was an early member of the machine learning systems research community and has been active in growing and forming the community. Previously, Sarah was a machine learning systems researcher at Microsoft Research NYC, where she worked on reinforcement learning systems and AI ethics. She cofounded the SysML research conference and the Learning Systems workshops. She holds a PhD in computer science from UC Berkeley, where she was advised by Dave Patterson, Krste Asanovic, and Burton Smith.
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