MLOps, AIOps

MLOps is a set of practices that combines ML, DevOps (development and operations), and data engineering to deploy and maintain ML models in production reliably and efficiently. MLOps aims to streamline the process of model development, deployment, monitoring, and management. It involves continuous integration and delivery (CI/CD) pipelines for ML, automated testing, model versioning, and performance monitoring, ensuring that ML models remain accurate and useful as they are updated and retrained over time.

• Integrated Approach: Learn how to combine ML, DevOps, and data engineering to streamline the deployment and maintenance of ML models.
• Automation Skills: Gain proficiency in continuous integration and delivery (CI/CD) pipelines, automated testing, and model versioning.
• Scalable Solutions: Understand how to build scalable and reliable ML systems that can be efficiently managed and monitored.