MLOps Engineer
Course in Hyderabad

1. 1

   Python Backend Development with AI

   Module 2 · 40 Hrs

   Operations starts with delivery. Concentrate on testing, Docker, CI/CD with GitHub Actions and cloud deployment basics, because every MLOps pipeline you build later reuses these pieces.

   What you study

   • Advanced Python and modular application architecture
   • FastAPI development and RESTful API design
   • Redis and background job queues
   • Testing and test-driven development
   • Docker containerization
   • CI/CD with GitHub Actions
   • Cloud deployment fundamentals

   Tools you use

   DockerGitHub ActionsPytestAWSAzure

   Hands-on lab

   Containerize a backend service with Docker and automate testing with GitHub Actions.

   See the full module →
2. 2

   Machine Learning Engineering

   Module 3 · 40 Hrs

   You cannot operate what you do not understand. Learn how models are trained, evaluated, packaged and monitored, and focus on pipelines, serving, retraining and experiment tracking with MLflow.

   What you study

   • Machine learning fundamentals
   • Model training and evaluation
   • Building ML pipelines
   • Deep learning fundamentals
   • Model serving and deployment
   • Model monitoring and retraining

   Tools you use

   MLflowScikit-learnFastAPIDocker

   Hands-on lab

   Track experiments and register models using MLflow, and set up monitoring for a deployed model.

   See the full module →
3. 3

   MLOps, LLMOps & AI Platform Engineering

   Module 6 · 30 Hrs

   This module is the centre of the role. Work through registry, versioning, regression tests, CI/CD for AI, tracing, IAM and secrets, and rollbacks, and treat evaluation as a gate a release must pass.

   What you study

   • Experiment tracking and model registry
   • Model and prompt versioning
   • Regression testing for AI systems
   • CI/CD for AI systems
   • Tracing and observability
   • Cloud IAM and secrets management
   • Scaling and rollbacks

   Tools you use

   MLflowGitHub ActionsDockerCloud PlatformMonitoring Tools

   Hands-on project

   AI Platform Operationalization. Apply MLOps/LLMOps practices — versioning, CI/CD, observability — to an existing AI service from the program.

   See the full module →
4. 4

   Agentic AI Engineering

   Module 5 · 40 Hrs

   Agent systems fail in new ways. Concentrate on evaluation, observability, retries, fallbacks and guardrails, so you can monitor and harden AI systems that make several calls per request.

   What you study

   • State and memory management
   • Routing and agent workflows
   • Human-in-the-loop design
   • Retries, fallbacks and guardrails
   • Agent evaluation and observability
   • Prompt injection defence

   Tools you use

   LangGraphLLM APIsFastAPIDocker

   Hands-on lab

   Evaluate agent performance and add observability for monitoring agent behaviour.

   See the full module →
5. 5

   Python & Technical Foundations

   Module 1 · 30 Hrs

   Linux and Git are your daily working environment. Use this module to become fast with the command line, virtual environments, Git workflows and debugging, and to write clear technical documentation.

   What you study

   • Virtual environments and dependency management
   • Git and GitHub version control workflows
   • Linux and command-line interface fluency
   • HTTP fundamentals and REST APIs
   • Debugging techniques for Python applications
   • Writing clear technical documentation

   Tools you use

   LinuxGitGitHubPython

   Hands-on lab

   Set up and manage isolated virtual environments for a multi-project workflow.

   See the full module →

What the programme covers for this role. The programme teaches MLOps and LLMOps as practical habits: pipelines, tracking, testing, tracing and safe releases on a cloud platform. It does not cover Kubernetes, infrastructure as code or large-scale data engineering, which teams often add on the job.