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Learning Paths

LLMs & Agents

Prompting, RAG pipelines, and agentic workflows.

Infrastructure & Systems

Hardware sizing, local deployment, and quantization.

Deep Learning & PyTorch

Neural architectures, PyTorch, Transformers, and RL.

Core Foundations

Python, linear algebra, calculus, and classical ML.

All Courses

Full catalog ordered by prerequisite sequence.

Infrastructure & Systems

Hardware sizing, local deployment, and quantization.

AI Infrastructure Basics

GPU architectures, memory hierarchy, VRAM sizing, and compute bandwidth fundamentals.

3 Courses

1

Understanding LLM Model Sizes and Hardware Requirements

Foundational

2

Planning and Optimizing AI Infrastructure

Intermediate

3

Getting Started With Kubernetes

Intermediate

Local LLM Enthusiast

Local inference engines (Ollama, llama.cpp), model weights management, and on-device runtime configuration.

3 Courses

1

Getting Started with Local LLMs

Foundational

2

Understanding LLM Model Sizes and Hardware Requirements

Foundational

3

Deploying Quantized LLMs for Efficient Inference

Specialist

AI Infrastructure Architect

Cluster compute topologies, high-speed interconnects (NVLink/InfiniBand), and multi-node systems.

4 Courses

1

Understanding LLM Model Sizes and Hardware Requirements

Foundational

2

Planning and Optimizing AI Infrastructure

Intermediate

3

Advanced AI Infrastructure Design and Optimization

Specialist

4

Advanced Compiler and Runtime Optimizations for ML Workloads

Expert

LLM Inference Engineering

Continuous batching, PagedAttention, KV cache management, speculative decoding, and vLLM deployment.

4 Courses

1

Understanding LLM Model Sizes and Hardware Requirements

Foundational

2

Practical Quantization for Large Language Models

Intermediate

3

Deploying Quantized LLMs for Efficient Inference

Specialist

4

LLM Compression and Acceleration Techniques

Expert

Efficient AI Deployment

Production serving pipelines, containerized GPU runtimes, auto-scaling, and latency-throughput trade-offs.

4 Courses

1

Docker and Containerization for ML Projects

Intermediate

2

FastAPI for ML Model Deployment

Intermediate

3

Deploying Quantized LLMs for Efficient Inference

Specialist

4

Introduction to ML Compiler Optimization

Intermediate

Compiler Optimization for ML

ML compilers (torch.compile, TVM), graph capture, kernel fusion, and hardware code generation.

3 Courses

1

Introduction to ML Compiler Optimization

Intermediate

2

Compiler Optimizations for Machine Learning

Specialist

3

Advanced Compiler and Runtime Optimizations for ML Workloads

Expert

MLOps Engineer

Automated training pipelines, model registries, artifact versioning, and continuous evaluation.

5 Courses

1

Getting Started with Git

Foundational

2

Introduction to MLOps

Foundational

3

Docker and Containerization for ML Projects

Intermediate

4

FastAPI for ML Model Deployment

Intermediate

5

Monitoring and Managing ML Models in Production

Specialist

LLM Ops Specialist

LLM observability, prompt tracing, latency and token cost telemetry, and automated evaluation.

4 Courses

1

Introduction to MLOps

Foundational

2

MLOps for Large Models (LLMOps)

Specialist

3

LangChain for Production-Ready LLM Applications

Specialist

4

Monitoring and Managing ML Models in Production

Specialist

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