roadmaps that work

structured paths that actually get you somewhere. track your progress, no account needed.

LLM Foundations

get the fundamentals right. no shortcuts, just real understanding

duration: 2 weeks

Week 1: Core Concepts

Week 2: Architecture & Training

RAG to Production

from prototype to production. real systems, real problems, real solutions

duration: 2 weeks

Week 1: RAG Fundamentals

Week 2: Production Deployment

Local LLM Deployment

run llms on your own hardware. privacy, cost control, and full ownership

duration: 3 weeks

Week 1: Model Selection & Quantization

  • Choosing the Right Model
  • Quantization Techniques (GGUF, AWQ)
  • Hardware Requirements & Optimization

Week 2: Deployment Frameworks

  • Ollama & LM Studio Setup
  • vLLM for Production Serving
  • Docker & Kubernetes Deployment

Week 3: Optimization & Monitoring

  • Performance Tuning
  • Cost Analysis & Resource Management
  • Monitoring Local LLM Systems

MLOps for LLMs

deploy, monitor, and iterate on llm systems. the complete production lifecycle

duration: 4 weeks

Week 1: ML Lifecycle & Versioning

Week 2: CI/CD for ML

Week 3: Infrastructure & Orchestration

Week 4: Monitoring & Drift Detection

AIOps & Observability

monitor ai systems in production. metrics, alerts, and observability that actually help

duration: 3 weeks

Week 1: Observability Stack

Week 2: Monitoring & Alerting

Week 3: Anomaly Detection & Automation

Fine-tuning & Evaluation

adapt models to your domain. when to fine-tune, when not to, and how to do it right

duration: 3 weeks

Week 1: Fine-tuning Fundamentals

Week 2: Training & Optimization

Week 3: Evaluation & Deployment

AI Agents & Tool Use

build agents that use tools, plan, and reason. multi-agent systems and autonomous ai

duration: 4 weeks

Week 1: Agent Foundations

Week 2: Memory & State

Week 3: Multi-Agent Systems

Week 4: Production Agents