You should have at least 2 years of experience in software engineering or DevOps, along with familiarity with CI/CD concepts, Git, and at least one major cloud platform (AWS, Azure, or Google Cloud). Basic experience with containers and the Linux command line is also recommended.

The curriculum covers Docker containerization, Kubernetes orchestration, Jenkins CI/CD pipelines, Terraform infrastructure as code, Ansible configuration management, and AI/ML deployment across AWS, Azure, and Google Cloud. You'll also learn MLOps practices, DevSecOps for AI workloads, monitoring and observability, cost optimization, and AI agent architecture using services like AWS Bedrock, Azure OpenAI Service, and Vertex AI.

Plan for approximately 15-20 hours per week over the 12-week duration. This includes video lectures, hands-on labs, project work, and self-study. The three capstone projects require significant hands-on effort, so consistent weekly engagement is essential for success.

The course is highly hands-on, with three industry-relevant projects: building a multi-cloud AI model deployment pipeline, architecting a scalable AI agent serving platform on Kubernetes, and creating a DevSecOps framework for AI applications. You'll work with real cloud environments, configure actual CI/CD pipelines, and deploy AI models to production-grade infrastructure.

Graduates are prepared for high-demand roles such as AI DevOps Architect, Cloud Solutions Architect, Distributed Systems Engineer, and DevSecOps Specialist. The combination of DevOps expertise with AI deployment skills is increasingly sought after as organizations scale their AI initiatives, making this a highly valuable career differentiator.

You'll gain hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and Ansible as core DevOps tools. On the cloud side, you'll work with AWS (Bedrock, SageMaker, CloudTrail), Azure (OpenAI Service, Machine Learning, Monitor), and Google Cloud (Vertex AI, Cloud Run, Cloud Monitoring), along with vector search and observability tools.

The Micro Degree course is an online LIVE course, where LIVE sessions will be conducted online on our Classroom platform. Prior to the start of the course, you'll receive preparatory material in the form of recorded content which can be access on the same platform.

In this course instructors will use English language for teaching.

Upon successful registration, you will receive a confirmation email on your registered email ID. In this email you will receive login details for your newly created account on the Edyoda Classroom platform (https://classroom.edyoda.com). Additionally, you will receive a PDF guide containing step-by-step instructions on how to utilize the platform to access live sessions and learning materials.

Our instructors are the industry experts with a minimum working experience of 10 years with a strong technical and teaching background. They bring industry knowledge and practical expertise to the course.

Yes, the course includes online assignments, quizzes, and a final project to reinforce your learning and assess your proficiency in AI DevOps Architect.

Yes, you can interact with instructors and fellow students through discussion forums, live Q&A sessions. We encourage a supportive learning community.

We offer a 100% money-back guarantee to ensure your complete satisfaction. If you're not satisfied, you can request a full refund within 3 days of purchase or before the second session, whichever comes earlier. Simply contact our support team(support@edyoda.com) with your purchase details, such as the order ID or email address, and share your reason for the refund. Requests made after 3 days or after the second session will not be eligible for a refund. There are no hidden charges, you will receive the full amount paid. Refunds are processed within 7–10 business days and credited back to your original payment method.