Explore the intricacies of MLOps in this insightful panel discussion featuring industry experts. Gain valuable perspectives on operationalizing machine learning, including best practices, implementation challenges, and emerging trends. Learn how MLOps addresses challenges in large-scale systems, the role of open-source tools like Docker and Kubernetes, and the importance of security considerations. Discover practical strategies for successful MLOps projects, version control best practices, and solutions for concept drift. Benefit from the diverse expertise of panelists from Amazon, Data Science Dojo, and AWS as they share real-world experiences and offer guidance on navigating the evolving MLOps landscape.
Overview
Syllabus
- Introduction
- Speaker Introduction
- Challenges in ML operationalization and ML Ops solutions
- ML Ops and ultra-large scaling ad auction systems
- Overlooked issues and how ML Ops can prevent them
- A story highlighting the importance of ML Ops
- Concept drift and ML Ops
- Open source's role in accelerating ML Ops adoption
- Docker and Kubernetes in ML Ops
- Best practices for version control in ML Ops
- Addressing company refusal to adopt open source
- Security concerns and other issues with open source in ML Ops
- Closing remarks
Taught by
Data Science Dojo
