In this speaker session, we will explore the role of DevOps in the deployment and maintenance of machine learning models. We will discuss how DevOps practices such as continuous integration, continuous deployment, and infrastructure as code can be applied to the ML workflow to improve collaboration, efficiency, and scalability. Attendees will learn about tools and techniques for automating the deployment and monitoring of ML models, as well as strategies for managing and updating models in production. The session will also cover best practices for testing, debugging, and troubleshooting ML models, with a focus on maintaining high accuracy and performance in production environments. By the end of the session, attendees will have a better understanding of how to leverage DevOps to optimize the deployment and maintenance of ML models.

Session will likely consists of the following segments:

- Explanation of how DevOps practices can improve collaboration, efficiency and scalability in ML model deployment and maintenance
- Discussion on the importance of version control and configuration management in ML models
Understanding of the role of containers and container orchestration in ML model deployment
- Explanation of how to implement Continuous integration and Continuous Deployment for ML models
- Overview of monitoring and logging practices specific to ML models
- Strategies for A/B testing and rollbacks in ML model deployment
- Best practices for maintaining model performance in production
- Understanding of how to manage model drift and retraining in production environments
- Discussions on how to handle data privacy and security in ML model deployment and maintenance.