Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA

Automating DevOps for machine learning

Diego Oppenheimer (Algorithmia)
11:00am11:40am Wednesday, March 27, 2019
Secondary topics:  AI and Data technologies in the cloud, Automation in data science and big data, Model lifecycle management

Who is this presentation for?

Data Scientists, Machine Learning Engineers, DevOps, Project and Product Management

Level

Intermediate

Prerequisite knowledge

This talk will assume familiarity with systems like Docker, APIs, Kubernetes, and Cloud Hosting.

What you'll learn

This session will help you understand:

  • Major challenges of productionizing ML models
  • Common roadblocks on the path to scalable deployment
  • How to solve these problems

Description

You’ve spent hundreds of hours cleaning your data, engineering features, and training and tuning your model to pinpoint accuracy. But now it’s time to deploy your model into production.

Whether you lead a team of data scientists or are one yourself, you know how time-consuming and problematic deployment can be. Language and environment incompatibilities, manual and duplicative processes, out-of-control costs, and poor communication can destroy all the work you’ve put into building models and slow your machine learning efforts.

Learning common deployment architectures for machine learning in the real world and understanding how to build your model for a production environment can help you avoid pitfalls when scaling up. We’ll cover common problems and solutions and share best practices from leading organizations that have solved the deployment headache.

Photo of Diego Oppenheimer

Diego Oppenheimer

Algorithmia

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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