Explores KitOps, an open source project that bridges the gap between DevOps and machine learning pipelines by allowing you to leverage existing DevOps pipelines for MLOps tasks. KitOps introduces the concept of ModelKits, standardized packages encapsulating all critical ML project components.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
• Importance of CI/CD in ML lifecycle
• Designing CI/CD pipelines for ML models
• Automating model training, deployment, and monitoring
• Overview of tools and platforms used for CI/CD in ML
