What nobody told you about machine learning in the hybrid cloud
Who is this presentation for?Data engineers, data architects, developers
DevOps workflows, continuous integration pipelines, and cloud native infrastructure are hot topics among today’s machine learning practitioners, and with good reason: these combine to tame the complexity of production machine learning systems and let you use applications and workloads anywhere.
Sophie Watson and William Benton explain these benefits and show you why you should strongly consider developing your next machine learning system for the hybrid cloud. Their focus, however, is on the challenges and trade-offs you’ll face as your use of cloud native infrastructure matures: data scientists want the flexibility to install the latest packages, but your security team wants to vet all libraries; practitioners want a rapid feedback loop, but stakeholders demand complete reproducibility; everyone wants the benefits of DevOps, but no one wants to change the way they work today. You’ll discover the spectra of approaches you can use to apply DevOps workflows and contemporary infrastructure platforms to make machine learning systems easier to develop and maintain. Sophie and William identify pragmatic solutions for real problems backed by their real-world experience helping build machine learning systems in a variety of industries. Along the way, you’ll understand why the opportunities of the hybrid cloud outweigh the challenges you’ll face on your journey.
- Familiarity with Kubernetes or containers
What you'll learn
- Understand how to apply DevOps workflows and Kubernetes to make machine learning systems easier to develop and maintain
- Discover how to balance demands of flexibility, reproducibility, and security while supporting and collaborating with data scientists
- Learn lessons from building machine learning systems on Kubernetes in the real world
Sophie Watson is a senior data scientist at Red Hat, where she helps customers use machine learning to solve business problems in the hybrid cloud. She’s a frequent public speaker on topics including machine learning workflows on Kubernetes, recommendation engines, and machine learning for search. Sophie earned her PhD in Bayesian statistics.
William Benton is an engineering manager and senior principal software engineer at Red Hat, where he leads a team of data scientists and engineers. He’s applied machine learning to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His focus is investigating the best ways to build and deploy intelligent applications in cloud native environments, but he’s also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.
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