Agentic AI & MLOps Engineering for Production AI Systems
Design, build, deploy, monitor and scale agentic AI systems with MLOps discipline. Learn how to move from prototypes to reliable AI services using agents, RAG, evaluation, observability and cloud deployment.
Built for developers, AI engineers, cloud engineers and technical teams moving to production AI.
Go beyond notebooks. Build deployable AI systems with monitoring, evaluation, versioning, governance and real-world reliability.
AED 5999
Enroll in the Agentic AI & MLOps Engineering program.Become the engineer who can deploy AI, not just demo it.
Agentic systems are moving from experiments to real business infrastructure. This program helps you understand how agents, RAG pipelines, model evaluation, orchestration and MLOps practices work together to create reliable production AI applications.
Learn the full path from prototype to monitored AI service.
You will build agentic workflows, connect tools and memory, evaluate outputs, containerize services, deploy APIs and monitor performance under real-world constraints.
Enroll NowAgentic Systems
Build agents with planning, tools, memory and task execution.
RAG Engineering
Design retrieval pipelines with vector databases and evaluation.
MLOps Workflow
Version, test, deploy and monitor AI models and applications.
Cloud Deployment
Deploy AI systems as APIs, services and internal tools.
What You Will Build
Concrete engineering outcomes you can show, extend and reuse.
Production Agent
An AI agent that uses tools, memory, external data and controlled decision flows.
Evaluated RAG Pipeline
A retrieval system with embeddings, chunking, vector search and quality checks.
Deployed AI API
A containerized AI service deployed as an API or internal application.
Monitoring Dashboard
Basic observability for cost, latency, response quality and workflow failures.
Who Should Attend
Built for technical professionals who want production-ready AI skills.
Ideal For
Not a Fit If
Tools and concepts covered.
Learn the practical stack behind reliable agentic AI deployment.
LangChain
Chains, tools, memory and LLM application workflow patterns.
LangGraph
Stateful agent graphs and controlled multi-step orchestration.
Vector DBs
Embeddings, vector search, RAG quality and retrieval tuning.
Docker
Containerize AI apps for portable, reliable deployment.
Monitoring
Latency, cost, quality, logs, failures and basic observability.
Agentic AI & MLOps Engineering
A hands-on engineering program for technical professionals building reliable AI systems with agents, RAG, deployment, evaluation and monitoring.
A practical path from AI prototype to production deployment.
Modules include architecture, debugging, evaluation, deployment and MLOps trade-offs used in real AI systems.
Agentic AI Foundations & System Design
Understand the architecture behind agents, tools, memory and workflow orchestration.
- LLM application architecture and agent design patterns
- Planning, tool-use, memory and controlled execution
- Prompt reliability, structured outputs and guardrails
- Failure modes in agentic workflows
RAG Engineering, Evaluation & Data Pipelines
Build retrieval systems that work with external knowledge and measurable quality.
- Chunking strategies, embeddings and vector search
- RAG relevance, hallucination reduction and testing
- Data ingestion and document pipeline design
- Evaluation metrics and quality checks
MLOps for AI Applications
Apply MLOps practices to LLM and agentic systems.
- Versioning prompts, models, datasets and workflows
- CI/CD thinking for AI services
- Cost, latency and reliability monitoring
- Security, governance and access boundaries
Cloud Deployment & Capstone
Deploy an AI system as an application, API or internal business tool.
- Dockerizing AI applications
- Deployment on cloud environments
- Observability dashboard and logs
- Final capstone project and engineering review
CERTIFICATE
Participants graduate with a certificate and a production-ready AI project demonstrating agentic AI and MLOps engineering skills.
Verified
Graduate with a deployable AI project.
Upon completing the program, you will receive a certificate and a capstone project that demonstrates practical AI engineering, deployment, monitoring and production-readiness.
Enroll NowFrequently Asked Questions
Clear answers before you enroll.