Best AI engineering courses for software engineers in 2026
Checked October 30, 2026
You already write production code. The course to take depends on the gap you want to close first, so the table below starts from the gap, and every entry, Fanout's included, says what it leaves out.
Which course fits which gap
| If you | Start with |
|---|---|
| ship LLM features and need evaluation, adaptation and deployment as one process | AI Engineering, Chip Huyen's book |
| want the mechanics under your app: vectors, retrieval, attention, the KV cache, reasoning, agents and MCP | Fanout AI Engineering |
| want free, code-first lessons on the Hugging Face stack, through fine-tuning | Hugging Face LLM Course |
| want a free, open-source curriculum that builds each algorithm by hand | AI Engineering from Scratch |
| prefer short courses on one tool at a time | DeepLearning.AI short courses |
| need a certificate to show an employer | IBM AI Engineering Professional Certificate |
| are new to Python | The AI Engineer Course on Udemy |
How we compared
We asked the same five questions of every entry, Fanout's included: who it is written for, what it teaches beyond calling an API, how you practise, what it costs, and what it leaves out.
The courses and books
365 Careers on Udemy
The AI Engineer Course 2026: Complete AI Engineer Bootcamp
- Format
- Video course with coding exercises, quizzes and a certificate of completion
- Length
- 448 lectures, 29 h 54 min of video
- Price
- Set by Udemy and changes often; 30-day money-back guarantee
- What you get
- AI concepts, Python from the basics, NLP in Python, LLMs and transformers, LangChain, vector databases with Pinecone, and speech recognition.
- Best for
- Someone new to Python or AI who wants one long guided course.
- Trade-off
- It starts from zero. Its requirements say "No prior experience is required", so a working engineer will skip much of it.
Chip Huyen
AI Engineering
- Format
- Book, O'Reilly Media, 2025; also on Kindle, Audible and the O'Reilly platform
- Price
- Varies by store and country
- What you get
- Building applications on foundation models: whether to build at all, evaluating outputs (including using AI to judge AI), hallucinations, prompt engineering, RAG, agents, when to fine-tune, how much data you need, making models faster and cheaper, and feedback loops.
- Best for
- Engineers and leads who ship AI features and want one reference for the whole process, evaluation above all.
- Trade-off
- Huyen describes it as not a tutorial book, with few code snippets, so you supply the practice.
DeepLearning.AI
Short courses
- Format
- Short video courses with code examples, many built with a company partner
- Length
- Most run 1 to 2 hours. Fast & Efficient LLM Inference with vLLM (with Red Hat) runs 1 h 38 min; Efficiently Serving LLMs (with Predibase) runs 2 h 9 min
- Price
- Efficiently Serving LLMs is free to enrol during DeepLearning.AI's platform beta; graded assignments and certificates need a Pro membership
- What you get
- One tool or technique per course. The two examples above cover quantization, vLLM serving, KV caching and serving many LoRA adapters at once.
- Best for
- Filling one gap at a time, in an afternoon.
- Trade-off
- Each course is short and many centre on one partner's product, so you assemble your own path.
Fanout
AI Engineering
- Format
- Self-paced lessons: 31 of the 43 have a video (about 3.8 hours in all) and the rest are readings. Part of one Fanout plan with Inference Engineering, System Design, AI Research and ML Math
- Length
- 43 lessons in nine modules; 21 lessons are free, including the first two modules
- Price
- $227 a year, $39 a month or $256 once in the US; ₹7,200 a year, ₹1,700 a month or ₹9,300 once in India
- What you get
- A start-here module on the AI engineering stack and one model call end to end; embeddings and retrieval (vector compression, indexing, search execution, a vector database's internals); LLM foundations and RAG; LLM internals (positional embeddings, attention, the KV cache); a transformer walkthrough where you trace the tensor shapes; optimizations (PagedAttention, mixture of experts, FlashAttention); model trade-offs (quantization, sparse attention, distillation, speculative decoding); reasoning and tool use; context engineering, the agent loop and MCP. Several modules end with a worked exercise, such as a KV-cache allocation.
- Best for
- Software engineers who build on models and want to know what the model and the retrieval stack are doing, so they can debug cost, latency and quality.
- Trade-off
- No module on evaluation or fine-tuning yet. Serving models in production is in the separate Inference Engineering course, on the same plan. Fanout purchases are final, so start with the free lessons.
Hugging Face
LLM Course
- Format
- Online course with notebooks you run in Google Colab or Amazon SageMaker Studio Lab; Apache 2.0 licence
- Length
- 12 chapters, each planned as a week of 6 to 8 hours
- Price
- Free, without ads
- What you get
- Transformers, Datasets and Tokenizers, classical NLP tasks, building and sharing demos, then curating datasets, fine-tuning LLMs and building reasoning models in chapters 10 to 12.
- Best for
- Engineers who want to work with open models on the Hugging Face stack.
- Trade-off
- By its own introduction it needs good Python and is better taken after an introductory deep learning course.
IBM on Coursera
AI Engineering Professional Certificate
- Format
- 13-course series, self-paced, intermediate level; shareable certificate
- Length
- About 4 months at 10 hours a week
- Price
- Included in Coursera Plus
- What you get
- Courses 1 to 6 teach machine learning with scikit-learn and deep learning with Keras, TensorFlow and PyTorch, ending in a capstone. Courses 7 to 13 cover generative AI and transformers, fine-tuning LLMs, and RAG and agents with LangChain.
- Best for
- A credential for a move into ML engineering.
- Trade-off
- The first six courses, 108 of the 168 hours Coursera lists across its courses, are classic machine learning and deep learning, so the LLM part comes last.
Rohit Ghumare and contributors
AI Engineering from Scratch
- Format
- Open-source curriculum (MIT licence) that runs on your machine; lessons in Python, TypeScript, Rust or Julia
- Length
- 20 phases and 542 lessons listed
- Price
- Free
- What you get
- From linear algebra to LLM engineering, MCP and agents. Each lesson runs the same loop: read the problem, derive the math, write the code, run the test. Backprop, tokenizers and attention are built by hand before PyTorch appears.
- Best for
- Engineers who learn by implementing and want no paywall.
- Trade-off
- A local, code-first curriculum: you need to set up and run the exercises yourself.
Under the API or around it?
AI engineering has two halves. One is the product process: choosing a model, evaluating outputs, adapting with prompts, RAG or fine-tuning, and controlling cost. Chip Huyen's book covers that half end to end. The other half is the machinery: how embeddings and vector indexes behave, what attention and the KV cache cost, why a quantized model gets faster or worse. That half is how you find out why a system is slow or expensive, and it is the half Fanout teaches. Evaluation, from the first half, is how you find out it is wrong. Most engineers need both.
When serving becomes your problem, see the best LLM inference courses, compared.
For customer-facing deployment roles, compare forward deployed engineer programmes against the skills the job asks for.
Questions
Do I need machine learning before AI engineering?
Not to start. Fanout's course and Chip Huyen's book both begin from building on existing models. IBM's certificate takes the classic machine learning route first.
Which of these are free?
AI Engineering from Scratch and the Hugging Face LLM Course are free. DeepLearning.AI's Efficiently Serving LLMs is free to enrol during its platform beta. 21 of the 43 lessons in Fanout's AI Engineering course are free, including the first two modules.
Which of these teach evaluation?
Chip Huyen's book covers evaluation in depth, including using AI to judge AI outputs. Fanout's AI Engineering course has no evaluation module yet.
Read the first two modules free
Start with What AI engineering is, then open the vector search lesson and the RAG chunking lab.