LLMOps Learning Path

Learning Path Modules

Module 1

Foundations for LLMOps

Python Linux Git

Build the essential technical foundation for working with LLMs in production.

Python Fundamentals

Variables, Data Types, Operators
Control Flow and Functions
Modules and OOP Concepts
Essential Libraries Overview

Linux Fundamentals

Core Components and Distros
Essential Bash Commands
Package Managers

Version Control

Git Basics and Workflows
Branching Strategies
Managing Sensitive Info

LLM Introduction

Transformer Architecture
Key Concepts and Providers
Understanding LLM APIs

Module 2

Developing and Interacting with LLMs

Prompt Eng APIs Frameworks

Learn to effectively develop applications using large language models.

Prompt Engineering

Techniques and Structures
Iterative Development
Versioning and Templating

LLM APIs & SDKs

REST API Interaction
Provider SDKs Usage
Secure Key Handling

LLM Frameworks

LangChain and LlamaIndex
Core Concepts
Building Basic Apps

Vector Databases

Embeddings Concept
RAG Workflow
Basic Implementation

Module 3

LLM Lifecycle Management

Tracking Evaluation Fine-tuning

Manage the complete lifecycle of your LLM applications.

Experiment Tracking

Challenges in LLM Dev
Adapting MLflow
Alternative Tools

Evaluation Strategies

Challenges and Metrics
Semantic Similarity
Human Evaluation

Fine-tuning

When to Fine-tune
PEFT Techniques
Data Preparation

Model Registry

Versioning Components
Deployment Stages
Managing Configs

Module 4

LLM Deployment Strategies

APIs Optimization Patterns

Deploy LLM applications effectively in production environments.

LLM APIs

FastAPI for LLMs
Input/Output Schemas
Async Handling

Inference Optimization

Challenges
Quantization
Specialized Servers

Deployment Patterns

Direct API Proxy
RAG Services
Agent-Based Systems

Interfaces

Streamlit
Gradio
FastAPI Backend

Module 5

Automation and CI/CD

CI/CD Testing Automation

Implement continuous integration and deployment for LLM applications.

CI/CD Overview

LLMOps Journey
Best Practices
Modular Code

Continuous Integration

Unit Tests
Integration Tests
Quality Checks

Continuous Deployment

Container Deployment
Rolling Updates
GitHub Actions

Multi-Component Apps

Docker Compose
Local Development
Service Communication

Module 6

Monitoring LLMs

Metrics Drift Tools

Implement comprehensive monitoring for LLM applications in production.

Monitoring Importance

Beyond Traditional Metrics
Challenges
What to Monitor

Techniques

Logging
Metrics Tracking
Statistical Monitoring

Tools

Prometheus & Grafana
LLM-Specific Platforms
Basic Alerting

Quality Monitoring

Input/Output Analysis
Drift Detection
User Feedback

Module 7

Advanced Topics

Responsible AI Security Cost

Explore advanced considerations for production LLM applications.

Responsible AI

Fairness and Bias
Transparency
Privacy

Security

New Attack Surfaces
Mitigation Strategies
Tools and Techniques

Cost Management

Pricing Models
Monitoring Usage
Optimization Strategies

A/B Testing

Implementing Tests
Success Metrics
Analyzing Results

Frequently Asked Questions

This learning path is designed for software engineers, data scientists, and ML engineers who want to operationalize large language models in production environments. It's suitable for both beginners looking to enter the field and experienced practitioners wanting to specialize in LLMOps.

Basic programming knowledge in Python is recommended. Familiarity with machine learning concepts is helpful but not required, as we cover the necessary foundations in the early modules.

The learning path is designed to be completed in approximately 12-16 weeks with a commitment of 8-10 hours per week. However, you can progress at your own pace and focus on the modules most relevant to your needs.

You'll be able to design, develop, deploy, and maintain production-grade applications powered by large language models. This includes building LLM APIs, implementing RAG systems, optimizing inference, setting up monitoring, and managing the complete LLM lifecycle.

While this learning path doesn't offer formal certification, you'll complete hands-on projects that demonstrate your skills. We recommend building a portfolio of these projects to showcase your LLMOps expertise to employers.

Master LLMOps

What is LLMOps?

LLM Operations

End-to-End Process

Emerging Field