The New SDLC: CI/CD in the age of machine learning
Who is this presentation for?Technology Managers and Executives, Data Scientists, Data Engineers, DevOps Engineerds
Machine Learning (ML) will fundamentally change the way we build and maintain applications. How can we adapt our infrastructure, operations, staffing, and training to meet the challenges of the new Software Development Life Cycle (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 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. This talk will help you understand the differences between the traditional and ML-driven SDLCs and build a process and stack to bring efficiency to emerging development.
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
Integrating with existing life cycle management tools
Prerequisite knowledgeConceptual understanding of the SDLC and CI/CD will enhance the experience.
What you'll learn
Diego Oppenheimer, founder and CEO of Algorithmia, is an entrepreneur and product developer with an extensive background in all things data. Prior to founding Algorithmia 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 Bachelors degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
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