Beginner

Deploying Production-Ready LLM & Agent Systems

An 8-week intensive program that prepares junior developers to architect, build, and deploy enterprise-grade LLM applications with confidence. Progress from foundational prompt engineering to building multi-agent systems with full observability, deployment pipelines, and cost optimization strategies. By program completion, graduates will deliver a portfolio demonstrating production-ready RAG systems, agentic workflows, and LLMOps practices that directly address the 73% hiring gap for deployment-capable junior developers.

Firas BayramMachine Learning

4 students enrolled

Modules — Structured path

Interactive AI lessons

Flexible

Self-paced

Beginner

Recommended experience

What You'll Learn

Construct zero-shot, few-shot, and chain-of-thought prompts that consistently generate structured outputs (JSON, tables) with 90%+ accuracy

Build end-to-end RAG pipelines with advanced retrieval strategies (hybrid search, query expansion, reranking) achieving 85%+ answer accuracy on production test sets

Design and deploy multi-agent systems with 3+ specialized agents using LangGraph for state management and inter-agent coordination with 70%+ task completion rates

Implement production-grade LLMOps practices including automated evaluation pipelines, real-time monitoring dashboards, and observability stacks tracking latency, cost, and hallucination frequency

Deploy containerized LLM applications to cloud platforms (AWS SageMaker, Hugging Face Inference Endpoints) with auto-scaling, achieving <3s p95 latency while maintaining costs under $100/month

Fine-tune open-source LLMs using parameter-efficient methods (PEFT/LoRA) on custom datasets with <16GB GPU memory and evaluate ROI against prompt-engineered solutions

Skills You'll Gain

134 skills

API authentication and SDK installationResponse parsing and provider comparisonConceptual modeling of transformer internalsPrompting strategies informed by LLM generation mechanicsPrompt engineering for structured tasksLLM output evaluation and iterationPrompt engineering for reasoningPerformance evaluation of prompting strategiesPrompt engineering for structured data

Program Structure

Hands-On with LLM APIs: OpenAI, Anthropic, and Hugging Face

LLM Mental Models: How Transformers Generate Text

Zero-Shot and Few-Shot Prompting Strategies

Chain-of-Thought Prompting for Complex Reasoning

Structured Output Generation with JSON Mode and Function Calling

Debugging Prompt Failures: Hallucinations and Context Limits

Model Selection Workshop: GPT-4 vs. Claude vs. Llama 3

Meet Your Expert

Firas Bayram

Machine Learning

Artificial Intelligence researcher and university instructor specializing in adaptive learning systems, large language models, and generative AI. Passionate about bridging cutting-edge AI research with real-world applications and helping leaders adopt modern practical, data-driven AI solutions.

Deploying Production-Ready LLM & Agent Systems

What You'll Learn

Skills You'll Gain

Program Structure

LLM Foundations & Prompt Engineering for Reliability

Retrieval-Augmented Generation (RAG) Fundamentals

Advanced RAG Techniques & Hybrid Search

Fine-Tuning LLMs with PEFT and LoRA

Building AI Agents with Tool Use and ReAct

Multi-Agent Systems and LangGraph Workflows

LLMOps - Evaluation, Monitoring, and Observability

Production Deployment, Optimization, and Scaling

Meet Your Expert

Firas Bayram

Ready to start learning?