Abstract: 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
Bio: Coming soon!