Machine Learning Engineer
Course in Hyderabad

1. 1

   Python & Technical Foundations

   Module 1 · 30 Hrs

   Machine learning work is mostly code. Concentrate on Python data structures, virtual environments, Git, debugging and basic tests, so your experiments stay organised and repeatable.

   What you study

   • Python programming fundamentals — syntax, data structures and control flow
   • Functions and object-oriented programming in Python
   • Virtual environments and dependency management
   • Git and GitHub version control workflows
   • Debugging techniques for Python applications
   • Basic automated testing

   Tools you use

   PythonGitGitHubVS Code

   Hands-on lab

   Debug and write basic automated tests for a small Python application.

   See the full module →
2. 2

   Machine Learning Engineering

   Module 3 · 40 Hrs

   This is the heart of the role. Spend the most time on features, training and evaluation, then on pipelines, serving and monitoring. Ask of every result: could I trust this on new data?

   What you study

   • Machine learning fundamentals
   • Supervised learning techniques
   • Unsupervised learning techniques
   • Feature engineering
   • Model training and evaluation
   • Building ML pipelines
   • Model monitoring and retraining

   Tools you use

   Scikit-learnPandasPyTorchMLflowJupyter

   Hands-on project

   Machine Learning Service. Train, evaluate and deploy Machine Learning models as production-ready services.

   See the full module →
3. 3

   Python Backend Development with AI

   Module 2 · 40 Hrs

   A model needs a home. Use this module to learn FastAPI, database basics, Docker and CI/CD, so you can put a trained model behind an API and test it automatically.

   What you study

   • FastAPI development and RESTful API design
   • PostgreSQL and MongoDB database fundamentals
   • Testing and test-driven development
   • Docker containerization
   • CI/CD with GitHub Actions
   • Cloud deployment fundamentals

   Tools you use

   FastAPIPostgreSQLDockerGitHub Actions

   Hands-on lab

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

   See the full module →
4. 4

   MLOps, LLMOps & AI Platform Engineering

   Module 6 · 30 Hrs

   Once a model is live, you need proof it still works. Concentrate on experiment tracking, model registry, regression tests, tracing and rollbacks, using MLflow as the anchor tool.

   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
   • Scaling and rollbacks

   Tools you use

   MLflowGitHub ActionsDockerMonitoring Tools

   Hands-on lab

   Track experiments and register models and prompts using MLflow-based versioning.

   See the full module →
5. 5

   Generative AI & LLM Application Engineering

   Module 4 · 40 Hrs

   Many ML teams now work with embeddings and language models. Focus on embeddings, vector search and how RAG answers are evaluated, since these reuse the evaluation habits you built earlier.

   What you study

   • Large language models
   • LLM APIs and prompt engineering
   • Embeddings and vector databases
   • Document ingestion and chunking
   • Reranking and RAG evaluation
   • PostgreSQL and pgvector

   Tools you use

   LLM APIsHugging FacepgvectorFastAPI

   Hands-on lab

   Evaluate and improve RAG quality using reranking and evaluation techniques.

   See the full module →

What the programme covers for this role. The programme teaches machine learning as an engineering skill: classical models, deep learning fundamentals, serving and monitoring. It does not include research-level maths, a dedicated computer vision course or distributed training on large clusters, so treat those as later steps.