An illustration representing AI, ML, LLM, MLOps, and LLMOps workflows.

6-Month Powerhouse Roadmap: ML, LLM, MLOps & LLMOps

Level up your skills with the definitive AI Engineering journey—mastering Machine Learning (ML), Large Language Models (LLMs), MLOps, and LLMOps through associate-to-advanced tooling, real-world pipelines, and a full suite of production projects. At the end, students emerge with a corporate-ready portfolio and practical expertise across both data science and modern GenAI engineering.

Months 1-2: Core Machine Learning Foundations

Month 1: ML Fundamentals & Python Mastery

  • Data wrangling and visualization (NumPy, pandas, seaborn)
  • Essential ML algorithms (scikit-learn): regression, classification, clustering
  • Feature engineering and model selection
  • Evaluation metrics and baseline deployment practices

Mini-Projects:

  • Titanic survival predictor
  • Image classifier on small custom dataset
  • Model selection leaderboard

Month 2: End-to-End ML Projects & Experiment Tracking

  • Model orchestration and pipelines (Jupyter, scikit-learn Pipelines)
  • Experiment management (MLflow, Weights & Biases basics)
  • Data versioning (DVC)
  • Introduction to cloud ML environments (SageMaker, Vertex AI, AzureML)

Mini-Projects:

  • ML experiment tracking and auto-logging
  • Reproducible pipeline for data drift detection

Months 3-4: Deep Learning & LLM Foundations

Month 3: Deep Learning and NLP Foundation

  • Neural networks with TensorFlow/PyTorch
  • CNNs, RNNs, Transformers introduction
  • Core NLP tasks: sentiment, NER, Q&A
  • Pretrained model fine-tuning (Hugging Face basics)
  • Data pipeline automation

Mini-Projects:

  • Image recognition with transfer learning
  • Sentiment analysis pipeline with automated deployment
  • Sequence-to-sequence text generator

Month 4: LLM Development, Prompt Engineering, and Applications

  • LLM architectures (GPT, Llama2, Mistral, open-source landscapes)
  • Hands-on with Hugging Face/Transformers and LangChain
  • Prompt engineering best practices
  • Fine-tuning and evaluation on custom datasets
  • Use-case builds: retrieval augmented generation (RAG), summarization, code/genAI tasks

Mini-Projects:

  • Prompt engineering leaderboard
  • Custom QA chatbot for documentation
  • Document search with vector databases (Pinecone, Weaviate)

Month 5: MLOps & LLMOps Associate-Level Productionization

Weeks 17-18: MLOps Essentials

  • MLOps life cycle: versioning, CI/CD for models, deployment automation
  • Kubeflow/Pachyderm basics, orchestrating reproducible pipelines
  • Model monitoring, data drift, and feedback loops
  • ML system security (role-based access, reproducibility, audit logging)

Mini-Projects:

  • CI/CD ML pipeline: automated retraining and redeployment
  • Model drift dashboard using Evidently AI

Weeks 19-20: LLMOps for Scalable GenAI Deployments

  • LLMOps lifecycle: data, model, inference, serving at scale
  • Automated fine-tuning pipelines (with Hugging Face Hub and cloud GPU)
  • Vector DB management and updating
  • Inference optimization (quantization, distillation)
  • Advanced monitoring: hallucination detection, usage analytics, privacy

Mini-Projects:

  • API-first Chatbot with scalable LLM serving
  • Automated LLM monitoring, alerting, and retraining triggers

Month 6: Corporate-Grade Capstone Projects Sprint

The final month is a hands-on project marathon: ship 12 production-ready, team-based capstones featuring real-world requirements, end-to-end pipelines, and scalable GenAI and MLOps/LLMOps stacks.

Capstone Projects Lineup

  • Multi-Cloud ML Pipeline: Cloud-agnostic, reproducible ML pipeline on AWS, GCP, or Azure with full CI/CD, automated retraining, and deployment.
  • LLM-Powered Customer Support Bot: Build, deploy, monitor, and scale a RAG-based chatbot integrating vector DB (Pinecone/Weaviate), prompt engineering, and monitoring in prod.
  • Automated Data Drift & Model Monitoring Platform: Live dashboards and alerting for ML/LLM drift, workflow-triggered retraining.
  • End-to-End MLOps with Kubeflow: Pipeline ingestion, training, validation, deployment, and versioning on Kubernetes.
  • GenAI Content Moderation System: LLM-based filtering, hallucination detection, safe output auditing, and logging.
  • Realtime Recommendation System: Scalable, low-latency recommendations with automated A/B testing and rollback.
  • LLM CI/CD & LLMOps: Secure, audited LLM model deployment flow, version tracking, experiment tracking, production-grade serving.
  • Explainable AI Platform: Build dashboards for SHAP/LIME explanations, drift, and transparency for ML and LLM predictions.
  • Secure Model API Gateway: API gateway with rate limits, RBAC, approval flows for ML/LLM serving in regulated environments.
  • Human-in-the-Loop AI Feedback System: Real-world feedback integration to model improvement pipelines for continuous learning.
  • Serverless ML/LLM Event Pipeline: Auto-triggered retraining or inference with Lambda, Cloud Functions; scalable, cost-optimized.
  • Business KPI AI Insights Board: Combine structured/unstructured reporting via model insights and LLM-generated summaries on a live Grafana/Streamlit dashboard.

Program Structure

  • Every lesson = coding + a hands-on project
  • Live code reviews, team sprints, and hackathons
  • Production-grade portfolio—ready to impress any employer

By graduation, students will:

  • Master practical ML/LLM building blocks and deployment tools
  • Be fluent in production MLOps/LLMOps workflows
  • Build and present 12 full-scale, corporate-ready projects