DataOps Data Architecture and AI Best Practices
DataOps Data Architecture and AI Best Practices

Abstract: 

Why are data analytic teams failing? It is a challenge to manage teams successfully in technically complex systems. The good news is that there is a centuries-long evolution of ideas that improve how people manage these systems, whether factories, software development teams, and data analytics professionals. It started with pioneers like W. Edwards Deming, lean manufacturing, and statistical process control. Gradually these ideas crossed into the technology space in the form of Agile, DevOps and now, DataOps. DataOps can help organizations eager to adopt AI and machine learning (ML) bridge the gap between data science and operations. This talk addresses the architectural, cultural and process considerations associated with creating an agile AI/ML data analytics environment.

DataOps is for data and analytic team leaders who desire to innovate and struggle to keep up with customer requests and let embarrassing data errors slip into production. DataOps architecture and processes deliver new business insights by enabling the rapid development and deployment of innovative, high quality data analytic pipelines. Chris will outline the steps to create a DataOps data architecture, including how to add tests, modularize and containerize, do branching and merging, use multiple environments, parameterize your process, use simple storage, and use multiple workflows to deploy to production with efficiency. He will also explain why “don’t be a hero” and ‘collaborate broadly’ should be the motto of analytic teams — emphasizing that while being hero can feel good, it is not the path to success for individuals in analytic teams.

Bio: 

Chris Bergh is the Founder, CEO, and Head Chef at DataKitchen. Chris is a leader of the DataOps movement. He has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Through these experiences, Chris realized that there had to be a better way to quickly deliver innovative analytics without errors, which led to the founding of DataKitchen He is the co-author of the ""DataOps Cookbook” and the “DataOps Manifesto,” and a regular speaker on DataOps at many industry conferences.