Shivay Lamba – Streamlining AI development and Deployment with KitOps | PyData Global 2024
www.pydata.org
As organizations increasingly integrate and adopt AI and machine learning internally, the challenge of maintaining separate pipelines for ML-powered systems and conventional software makes it difficult for DevOps teams to maintain these separate pipelines. This talk explores a unified approach to DevOps and MLOps, demonstrating how existing DevOps pipelines can be transformed into efficient MLOps pipelines using ModelKits with KitOps
We’ll begin by examining the reasons behind the traditional separation of DevOps and MLOps pipelines, including differences in project nature, required expertise, and the size and complexity of artifacts. We’ll then delve into the challenges posed by separate pipelines, such as increased costs, coordination difficulties, and accumulating technical debt. Thus the attendees will learn how to leverage open source tooling like KitOps to create a unified pipeline that accommodates both traditional software and ML-powered projects, ultimately leading to more efficient and cost-effective operations.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
