The new SDLC: CI/CD in the age of machine learning
Who is this presentation for?
- Technology managers and executives, data scientists, data engineers, and DevOps engineers
ML will fundamentally change the way we build and maintain applications. Diego Oppenheimer dives into how you can adapt your infrastructure, operations, staffing, and training to meet the challenges of the new SDLC without throwing away everything that already works.
After decades of software development, the industry has settled on a common set of roles, processes, and tools. Developers, DevOps engineers, QA engineers, and release engineers each understand their responsibilities, and continuous integration/continuous deployment (CI/CD) and version-control systems automate the workflow.
ML is the future of application development, but presently, most ML teams are flailing without any process—or trying to shoehorn their ML workflow into tools that don’t fit the requirements. You’ll leave with an understanding of the differences between the traditional and ML-driven SDLCs and build a process and stack to bring efficiency to emerging development. You’ll explore why traditional software development works, the unique challenges of deploying and managing ML models at scale, how leading companies have built modern ML lifecycle automation, and integration with existing lifecycle management tools.
- A working knowledge of SDLC and CI/CD (useful but not required)
What you'll learn
- Discover how ML introduces fundamentally new challenges that traditional operations and tools are not prepared to handle
- Learn how to avoid having to build an entirely new system to support ML models and how to adapt and reuse what already exists to build an ML pipeline
Diego Oppenheimer, founder and CEO of Algorithmia, is an entrepreneur and product developer with an extensive background in all things data. Previously, he 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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