LLM Engineering & Agentic AI Training Course
LLM Engineering & Agentic AI by Educad Academy is a practical, industry-focused program designed for developers, students, and professionals who want to go beyond AI prototypes and build real, production-ready AI systems.
This course provides hands-on training in designing, fine-tuning, and deploying Large Language Models (LLMs) using Hugging Face and modern AI engineering tools. You will also explore agentic AI systems, where models can plan, reason, and take actions autonomously, along with multimodal AI applications that combine text, images, and other data types.
Throughout the program, you will work on real world use cases that reflect how AI is being applied in modern businesses, from automation to intelligent assistants and scalable AI workflows.
By the end of the course, you will have a strong, job ready portfolio featuring deployed AI agents, multimodal systems, and production-level AI solutions that demonstrate your ability to build and deliver real-world AI products.
Course Objectives:
- Build strong practical skills in LLM Engineering, AI Development, and Agentic AI systems
- Learn to design, fine-tune, optimize, and deploy production-ready AI applications using modern tools
- Gain hands-on experience with Hugging Face, LoRA, QLoRA, FastAPI, and AI deployment frameworks
- Understand real-world AI system architecture, automation, and scalable AI solutions
Course Content:
Module 1: Introduction to LLM Engineering & AI Landscape
- Evolution of AI
- Open-source vs closed models
- Hugging Face ecosystem overview
- Real world AI use cases
Module 2: Hugging Face Core Stack
- Transformers (v5 architecture)
- Datasets & model hub
- Model cards & reproducibility
- Deploying demos on Spaces
Module 3: Model Selection & Evaluation
- Choosing between 100k+ models
- Reasoning vs instruction models
- Benchmark understanding
- Cost vs performance tradeoffs
Module 4: Efficient Inference & Optimization
- Quantization techniques
- vLLM, TGI, SGLang
- KV caching & batching
- Cost optimization strategies
Module 5: Fine-Tuning & Custom Models
- PEFT, LoRA, QLoRA
- Dataset creation & cleaning
- Evaluation pipelines
- Avoiding bias & data leakage
Module 6: Agentic AI Systems
- Tool calling agents
- Memory systems
- Multi step reasoning workflows
- Real world automation agents
Module 7: Multi-Agent Systems & MCP
- Agent-to-Agent (A2A) communication
- MCP servers
- Tool orchestration
- Security & access control
Module 8: Multimodal AI Systems
- Vision-language models
- Speech-to-text & text-to-speech
- Diffusion models
- Building combined AI systems
Module 9: Deployment & Production Systems
- FastAPI & Gradio apps
- Hugging Face Inference Endpoints
- Monitoring & logging
- Scaling AI systems
Module 10: Final Capstone Project
- Build an AI product
- Deploy with monitoring
- Portfolio packaging
- Demo presentation
Learning Outcomes:
- Design and implement LLM-based applications
- Build and deploy agentic AI systems
- Fine tune models using modern techniques
- Develop multimodal AI solutions
- Optimize and scale AI systems
- Apply AI engineering skills to real-world problems
- Develop scalable and efficient AI systems
- Integrate AI into business workflows
- Work independently on AI product development
International Student Fee: 500 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class(L.V.C) Online Training
