DevOps-Data Science Training Program

I am putting together an intensive, three-month program for an advanced cohort of engineers and need a seasoned mentor to lead it end to end. The core focus is DevOps excellence: guiding the group to master CI/CD pipelines, manage cloud infrastructure through Terraform-based IaC, and deploy, scale, and troubleshoot workloads on Kubernetes.

Because many of the engineers are looking to pivot into data-driven roles, the curriculum must also weave in key data-science topics. I want them to finish with working knowledge of machine-learning fundamentals, hands-on experience performing data analysis in Python, and exposure to big-data tooling that complements modern DevOps workflows.

Here is what I will look for from you:

• A detailed syllabus that blends CI/CD, Terraform, and Kubernetes with modules on machine-learning basics, Python-powered data analysis, and big-data technologies.
• Live, highly interactive sessions (demo-first, theory-second) supported by clear slide decks, lab guides, and ready-to-run Git repositories.
• Real-world projects the participants can build incrementally—starting with automated deployments, adding IaC, then container orchestration, and finishing with data-science pipelines.
• Weekly check-ins, code reviews, and personalised feedback so every engineer leaves with production-ready artefacts and demonstrable skills.
• Session recordings, reference documentation, and an evaluation rubric I can reuse for future cohorts.

The audience is already at an advanced level, so keep the pace brisk, the examples realistic, and the tooling current (GitHub Actions, Terraform ≥1.4, Kubernetes 1.28+, Python 3.11, popular ML libraries such as scikit-learn and Pandas). If this blend of DevOps rigour and data-science insight is in your wheelhouse, tell me how you would structure the journey and share any similar programmes you have delivered.