MLOps is the application of DevOps practices and principles to the machine learning (ML) process. In MLOps - DevOps for Machine Learning, DevOps engineers and testers will learn the foundational knowledge and practical skills necessary to integrate machine learning operations (MLOps) into existing AI Model development workflows.
This workshop covers the entire MLOps lifecycle, focusing on essential concepts, tools, and methodologies required to deploy and maintain machine learning models within DevOps environments.
Key takeaways from this class include:
- Gaining an in-depth understanding of MLOps principles and their integration with DevOps practices.
- Learning to set up automated data engineering pipelines.
- Tracking model experiments effectively.
- Implementing CI/CD pipelines for machine learning models.
- Establishing robust monitoring and retraining strategies.
- Navigating security and compliance considerations in MLOps.
Throughout this workshop, students will gain real-world context through practical hands-on exercises, such as setting up feature stores, implementing CI/CD processes for ML models, and deploying and monitoring models.
Who Should Attend
This workshop is ideal for DevOps engineers, software testers, and operations personnel looking to expand their skill set into MLOps. Professionals involved in software development, deployment, infrastructure management, quality assurance, or operations who wish to understand the unique challenges and best practices in deploying and maintaining machine learning models will benefit. It caters to individuals with a technical background in DevOps practices but with limited exposure to machine learning, aiming to bridge the gap between traditional DevOps workflows and the specialized requirements of MLOps.
