Operationalizing Machine Learning

Outerbounds

Patterns and Pain Points from MLOps Practitioners

Shreya Shankar is a computer scientist doing her PhD in databases at UC Berkeley. She was the first ML engineer at Viaduct, did research at Google Brain, and did software engineering at Facebook. In this fireside chat, Shreya joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to discuss her team’s recent paper Operationalizing Machine Learning: An Interview Study, and what they discovered about the common practices &amp; challenges across organizations &amp; applications in ML engineering.After attending, you’ll know about<ul><li>The main tasks that people do in the Production ML lifecycle;</li><li>Key properties of ML workflow and infrastructure that dictate how successful deployments will be;</li><li>The biggest pain points faced by people deploying ML models to production;</li><li>What strategies ML engineers employ to sustain model performance once deployed;</li><li>What the biggest opportunities for future MLOps tools and research are;</li></ul>And much more! The fireside chat will be followed by an AMA with Shreya and Hugo at slack.outerbounds.co.

Operationalizing Machine Learning

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Operationalizing Machine Learning

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