Learn AI engineeringby building real systems
A hands-on curriculum for engineers moving into AI. Build RAG systems and AI agents, train and deploy models, and prove they work with tests and evals.
30-day money-back guarantee, cancel anytime
900+ engineers, from teams at
“Best educational investment in my ML/AI journey”
The tutorials cover crucial topics with a perfect balance of theory and practical applications. The code examples are clean, well-documented, and immediately applicable to real-world scenarios.
From your first model call to production.
5 free lessons are marked. Read one before you decide.
M00Build Your First AI App7 lessonsNeuroMind - Chatbot with Memory2 free
Build a local-first AI assistant with streaming, memory, and a real backend
M01RAG and Context Engineering10 lessonsFinVault - RAG for Financial Documents1 free
Build document AI that shows sources, handles messy context, and can be evaluated
M02AI Agents and Workflows8 lessons1 free
Build agents and workflows with tool use, recovery loops, and failure traces
M03AI Systems Engineering8 lessons
Turn prototypes into systems with evals, datasets, fine-tuning, and LLMOps
- 01The AI Engineer Toolkit - APIs, structured output, tools
- 02Connect AI to External Systems - Model Context Protocol
- 03Build Your Own Dataset with Knowledge Distillation
- 04Beyond Benchmarks - Evaluating LLMs
- 05No More Manual Tweaking - Automated Prompt Engineering
- 06Train Your Model - Fine-Tuning LLM
- 07Build a Chatbot with Memory
- 08LLMOps - Deployment and Monitoring of LLMs
M04MLOps and Production Systems5 lessons
Package, validate, deploy, and monitor ML services that stay up under real traffic
M05AI/ML Foundations6 lessons1 free
Fill in the Python, Docker, ML math, and PyTorch fundamentals you actually need.
For engineers whose AI has to work.
You need Python, Git, a terminal, HTTP and JSON, basic SQL, and pytest. The lessons cover everything AI-specific.
- Software engineers moving into AI products
- ML engineers moving beyond notebooks
- Developers building RAG, agents, evals, or internal copilots
- Consultants who need AI patterns they can hand to clients
- Career-switchers who need a portfolio that holds up in interviews
- Complete beginners who have never written code before
- Anyone who wants a certificate without a repository a stranger can run
- Engineers who want a framework tour: here you build the loop, the harness, and the gate yourself
- Teams already operating models at scale who need GPU tuning, training, or fine-tuning depth
- People who want passive "watch and listen" content
“The math and code behind the latest LLMs”
Studying recent AI advancements can be overwhelming with knowledge scattered everywhere. ML Academy centralizes learning by teaching both the math and code behind the latest LLMs and agentic applications. It offers real-world projects, tutorials, notebooks, and an active Discord community ready to engage.
Taught by a production ML engineer.
- Years shipping ML systems in production
- 10+
- Users served by systems I shipped
- 100M+
- Engineers trained at MLExpert Academy
- 900+
I've deployed ML/AI systems serving millions of users. I've also seen promising prototypes collapse because nobody could prove the answer, trace the tool call, reproduce the failure, or monitor the service.
Every lesson here is written the way I would want a senior colleague to hand me a system: the failure first, then the fix, then the test that keeps it fixed.
Before you decide.
Something else? venelin@mlexpert.io
Prefer to explore first? Read a free lesson first
What if I get stuck on something?
You get Discord access for questions and debugging. Tutorials include expected outputs, checkpoints, and common failure modes.
Which plan includes live cohorts?
Lifetime membership includes access to live cohorts. Both plans include the academy curriculum, source code, session recordings, and community.
Can I upgrade from monthly to lifetime later?
Yes. Start monthly and upgrade from your profile. Your monthly subscription is cancelled automatically when the lifetime payment completes.
Do you cover evaluation and reliability?
Yes. You test retrieval quality, check grounding, evaluate tool use, trace failures, and monitor latency and cost.
Do I need a GPU or an API key?
A GPU is optional. Deterministic acceptance checks use mock models; portfolio demos and eval reports require a real model. Use a supported local model if your laptop can run it, or configure an OpenAI-compatible hosted endpoint. Hosted providers may require an API key and usage charges.
What skills do I need to start?
You should be able to write Python functions and handle exceptions, use a terminal and Git, read HTTP requests and JSON responses, write basic SQL queries, and run and interpret pytest assertions. Lessons provide setup commands and teach the AI-specific decisions.
How much time should I commit?
Plan for 5–10 hours per week. Focused students can complete the core path in 4–8 weeks and have portfolio artifacts ready.
What is your refund policy?
Every plan has a 30-day money-back guarantee. Email venelin@mlexpert.io within 30 days of your first charge for a full refund.
How often is the content updated?
The curriculum is updated as the production AI stack changes. Members get new content as it ships.
“Build confidence and thrive as AI/ML engineers”
It covers core concepts to ramp up people who have some software engineering experience. The libraries and tools help students go further, build confidence, and thrive as AI/ML engineers or make a career shift.
Ship your first AI system this week.
You keep every repository you build.
Included
- All 6 modules: 42 hands-on lessons and 2 capstone repositories
- Source code, repositories, and architecture diagrams
- Portfolio artifacts: apps, APIs, eval reports, dashboards, capstones
- Session recordings
- Discord community access
What happens next
- 01Pick the plan that fits you
- 02Get everything that is live, and each module the day it ships
- 03Run your first lesson today
30-day money-back guarantee. One email, full refund.