MLOps and LLM Learning Resources

Here's a comprehensive list of free MLOps and LLM learning resources:

MLOps Resources

Courses (Free Content)

MLOps Zoomcamp (DataTalks.Club)

• Comprehensive 6-module program covering experiment tracking, orchestration, deployment and monitoring

• Includes end-to-end project for certificate

• GitHub:github.com/DataTalksClub/mlops-zoomcamp

• Slack community support available

MLOps by Duke University (Coursera)

• Free to audit

• Covers MLflow, AWS SageMaker, Azure, Hugging Face

• Topics: LLM fine-tuning, ONNX deployment

• https://www.coursera.org/specializations/mlops-machine-learning-duke

MLOps Fundamentals (Great Learning Academy)

• Lifetime access once enrolled

• No certificate fee required for free course

• Topics: MLOps lifecycle, tools, frameworks, implementation

MLOps for Beginners (Udemy Free)

• End-to-end ML development process

• Model lifecycle management

• https://www.udemy.com/course/mlops-for-beginners/

LLM Resources

Structured Courses (Free)

LLM Course by Maxime Labonne

• Two roadmaps: LLM Scientist (building LLMs) & LLM Engineer (production applications)

• Topics: Fine-tuning, quantization, RAG, AI agents, deployment

• Interactive version with LLM assistant

• GitHub + Colab notebooks

Generative AI for Beginners (Microsoft)

• 21 lessons, self-paced

• Topics: Prompt engineering, responsible AI, LLMOps, chatbots, search tools

• GitHub repository with code samples

• https://github.com/microsoft/generative-ai-for-beginners

Generative AI with LLMs (DeepLearning.AI)

• ~3 weeks full-time

• Covers: Transformers, prompt engineering, fine-tuning, AWS deployment

• Practical hands-on approach

LLM Evaluation for AI Builders (Evidently AI)

• 3-hour course

• Topics: Evaluation methods, benchmarks, guardrails, synthetic data

• Certificate available for cohort participants

Resource Hubs

Hugging Face NLP Track

• Transformer architecture deep dive

• Fine-tuning, text summarization, Q&A, translation

• Free library access (Transformers, Datasets, Tokenizer)

LLM University (Cohere)

• 8 modules: LLMs, text representation, generation, deployment, semantic search, RAG, prompt engineering, tool use

• Suitable for beginners to advanced learners

LangChain & Vector Databases in Production (Towards AI/Activeloop)

• Free resource for production LLM applications

• Partnership: Intel Disruptor Initiative

Training & Fine-Tuning LLMs for Production (Towards AI/Activeloop)

• Free resource for intermediate learners

• Requires: Python knowledge, moderate compute

GitHub Repositories

LLM Course (mlabonne/llm-course)

• Roadmaps + Colab notebooks

• Three parts: Fundamentals, Scientist (building), Engineer (applications)

• Covers: Tokenization, attention, quantization, fine-tuning

• https://github.com/mlabonne/llm-course

Start LLMs (louisfb01)

• Complete guide for 2025-2026

• Curated free resources + practice projects

• https://github.com/louisfb01/start-llms

Supplemental Learning

Foundational Generative AI

• 2-week free course

• Topics: LangChain, vector databases, open-source models, deployment

Learn Prompting

• Free course on prompting techniques for LLMs

• Specific strategies for different models

Quick Learning Path

1. Start: Generative AI for Beginners (Microsoft) -> foundational concepts

2. MLOps Focus: MLOps Zoomcamp -> practical operations

3. LLM Deep Dive: LLM Course (Maxime Labonne) -> choose scientist or engineer track

4. Hands-on: LangChain & Vector Databases course -> production-ready skills

All materials are directly accessible. Most include Colab notebooks for immediate practice.

Manuela Schrittwieser – Full-Stack AI Engineer and Tech Writer