Agentic AI & LLM Ops
From machine-learning foundations to autonomous, multi-agent production systems. Master the stack that is reshaping enterprise AI in 2026.
Why Agentic AI & LLM Ops?
Generative AI has moved past prompts and prototypes. The next frontier belongs to engineers who can deploy autonomous agents, manage retrieval pipelines, govern costs, and keep systems reliable at scale.
This 30-week advanced programme is built for exactly that transition: from notebook experiments to production-ready ML and agent systems that business teams can trust.
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Dual Architecture
Applied AI foundations (W1–15) + Agentic AI specialisation (W16–30) in one cohesive journey.
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Production-First Mindset
Every module ends with deployable artifacts: APIs, monitoring dashboards, cost reports, and model cards.
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Agentic Depth
Deep coverage of orchestration, memory, tool-use, and multi-agent design using modern frameworks.
The Skills Gap Is Your Opportunity
Most teams still struggle to move AI from experiment to revenue. This programme closes the execution gap.
What Companies Need
- check_circle Deploy LLMs in production environments
- check_circle Build autonomous agents with governance
- check_circle Scale AI economically and measure ROI
- check_circle Build enterprise RAG over private data
What Most Programmes Teach
- cancel Theoretical ML with limited deployment
- cancel Notebook experiments that never ship
- cancel Generic certificates without portfolios
- cancel One capstone, if any
8 Courses. 30 Weeks. One Transformation.
A carefully sequenced path from production ML to autonomous multi-agent systems.
Programming for ML & AI (Weeks 1–4)
Write robust code for data preprocessing, exploration, and feature engineering. Master Python for AI, NumPy, Pandas, SQL, visualisation, and version control.
Machine Learning & Deep Learning (Weeks 5–10)
Train, evaluate, and interpret ML/DL models for real business impact. Covers regression, classification, ensembles, neural networks, CNNs, RNNs, and time-series forecasting.
MLOps & Production Systems (Weeks 11–13)
Deploy ML with monitoring, cost control, and governance. Learn containerisation, FastAPI serving, experiment tracking, drift detection, A/B testing, and ROI calculation.
Capstone A: End-to-End ML System (Weeks 14–15)
Build and deploy a complete predictive system with model selection, hyperparameter optimisation, registration, monitoring, and explainability reporting.
LLM Foundations & RAG Systems (Weeks 16–20)
Build production RAG with governance and cost optimisation. Covers transformers, embeddings, vector databases, chunking, hybrid search, fine-tuning, and retrieval evaluation.
AI System Design Principles (Weeks 21–23)
Design scalable, secure, and cost-efficient LLM systems. Learn token economics, evaluation benchmarks, caching, load balancing, prompt-injection defence, and human-in-the-loop workflows.
Agent Frameworks & Orchestration (Weeks 24–28)
Design multi-agent systems with planning, memory, and supervision. Covers ReAct, tool-use, LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, telemetry, and AI compliance.
Capstone B: Production Agentic AI System (Weeks 29–30)
Deliver a fully functional autonomous assistant with architecture diagrams, governed RAG, monitoring dashboard, cost report, and security review.
What You Will Build
Leave with a GitHub-ready portfolio of production systems, not just certificates.
Deployed ML APIs
Containerised models served via FastAPI with CI/CD pipelines, monitoring, and live endpoints you can demo.
Enterprise RAG Pipelines
Retrieval systems over private documents with chunking, hybrid search, citation, and evaluation frameworks.
Autonomous Agent Crews
Multi-agent workflows with planning, memory, tool-use, and governance controls for real business tasks.
Monitoring Dashboards
Track model drift, latency, cost, and quality with dashboards and alerting ready for stakeholders.
Business Documentation
ROI calculations, model cards, architecture diagrams, and executive defence decks for every capstone.
GitHub Repositories
Clean, tested, reviewed codebases with documentation that hiring managers and clients can evaluate.
Two Capstones. Double the Impact.
From model to market — build twice, learn twice, and showcase twice.
Capstone A: End-to-End ML System
Theme: Predictive Maintenance
Develop a functioning ML pipeline with data ingestion, feature engineering, model training, HPO, registration, drift detection, and monitoring.
- check Data pipelines & feature stores
- check MLOps & model workflows
- check Monitoring & explainability
Capstone B: Agentic AI System
Theme: Autonomous Multi-Agent Assistant
Design and deploy a multi-agent system with governed RAG, memory, tool-use, telemetry, cost controls, and a security review.
- check Agent orchestration with LangGraph
- check RAG with governance & citations
- check Cost, security & performance dashboards
Who Is This Programme For?
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Software Engineers
Looking to transition from application development to AI/ML engineering roles.
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Data Scientists & Analysts
Ready to move beyond notebooks and into production systems, MLOps, and LLM deployments.
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AI Product Managers
Who need hands-on fluency with agent systems, token economics, and AI system design.
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Final-Year Graduates
With programming fundamentals who want to start their career at the AI frontier.
Weekly Commitment
Designed for working professionals and ambitious learners alike.
Live Classes
Instructor-led sessions every week
Hands-on Labs
Guided projects and assignments
Study Material
Readings, videos, and reference docs
Doubt Support
Mentor-led Q&A and code reviews
Ready to Build Autonomous AI Systems?
Enquire now to receive the detailed brochure, upcoming cohort dates, and a free consultation with our admissions team.