This MLOps Training program is designed to bridge the gap between Machine Learning development and production deployment. Participants will learn how to automate, deploy, monitor, and manage machine learning models throughout their lifecycle using industry-standard tools and best practices. The course covers ML pipelines, version control, CI/CD for Machine Learning, containerization with Docker, orchestration with Kubernetes, cloud-based deployments, model monitoring, and governance. Through hands-on labs and real-world projects, learners will gain practical experience in building scalable, reliable, and production-ready ML systems. By the end of the training, participants will be equipped with the skills needed to streamline ML workflows and successfully operationalize AI solutions in enterprise environments.
Master the principles and practices of Machine Learning Operations (MLOps) to efficiently deploy, monitor, and manage machine learning models in production environments. This hands-on training covers model lifecycle management, CI/CD for ML, automation, containerization, cloud deployment, monitoring, and best practices to help you build scalable, reliable, and production-ready AI solutions.
Download the course curriculum PDF or contact us for the complete training plan.
03 WEEKS Learn AWS server setup, EC2 basics, hosting workflow, and practical cloud deployment.
04 WEEKS Build practical skills in AWS Solution architect with guided training and hands-on practice.
02 WEEKS Manage relational databases on AWS with backups, monitoring, scaling, and access control.
04 WEEKS Master S3 buckets, static hosting, permissions, lifecycle rules, and storage best practices.
