You constructed data warehouses and filled your data lake. You hired data scientists and trained amazing models. But when it was time to make those models work, you found your tech stack and DevOps processes really didn’t work for ML.
Models require specialized hardware, produce spiky compute cycles, and are written in different languages than the models and applications that consume them. That all adds up to a mess of manual labor, broken deployments, and spiraling costs, and it means your ML investments are generating losses.
Here’s the good news: there’s a path forward. ML leaders such as Google, Uber, and Facebook have led the way, building AI-specific model deployment and management systems–and they’re working. But what does that mean for the rest of us?
In this talk, Diego Oppenheimer, CEO of Algorithmia, will draw upon his work with thousands of developers across hundreds of organizations and discuss the tools and processes every business will need to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science.
Diego Oppenheimer is the founder and CEO of Algorithmia. An entrepreneur and product developer with extensive background in all things data, Diego has designed, managed, and shipped some of Microsoft’s most used data analysis products, including Excel, Power Pivot, SQL Server, and Power BI. Diego holds a bachelor’s degree in information systems and a master’s degree in business intelligence and data analytics from Carnegie Mellon University.
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