🚀 What You’ll Learn

• Design and deploy robust CI/CD pipelines for ML and software projects using DevOps principles

• Build, train, version, and deploy machine learning models into production using MLOps best practices

• Utilize multi-cloud platforms (Azure, AWS, Google Cloud, etc.) for scalability, reliability, and performance

• Monitor, alert, and maintain applications & models in production — ensuring reliability and efficient updates

• Automate infrastructure provisioning with Infrastructure as Code (IaC) tools

• Integrate security, compliance, and governance into workflows (DevSecOps / MLOps)

• Debug, optimize, and troubleshoot real-world production issues

🧩 Course Structure

✅ Who Should Take This

• Data Engineers, ML Engineers, DevOps professionals

• Software developers wanting to upskill in data & ML pipelines

• Power BI / Data Analysts looking to expand into engineering & model deployment

• Anyone aiming for high-paying cloud/MLOps roles and production deployments

🎯 Outcome & Job Roles

After completion, you’ll be qualified for roles like:

• MLOps Engineer / Specialist

• CloudOps Engineer / DevOps Engineer

• Machine Learning Engineer (production focus)

• Data Pipeline / Platform Engineer