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ML & AI Engineering

From Python basics to production ML and AI systems

14 phases. 92 lessons. 92 labs. 9 projects. Python through SQL, classical ML, deep learning, transformers, LLM engineering, RAG, agents, evaluation, deployment, and cloud. You build real systems, deploy them, and graduate with a portfolio that proves you can do the job.

14 phases · 92 lessons · 92 labs · 9 projects

No prerequisites — this is a starting track.

Outcomes you will have by the end

• 9 GitHub repos with real eval reports — Each project ships with architecture docs, RAGAS faithfulness scores, and a case study you can walk through in any technical interview.
• 1 live deployed system — The capstone is deployed on AWS with Docker, CI/CD, and monitoring. A public URL you can share with hiring managers, not just a localhost demo.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Includes the specific skills, phases, and projects you completed.
• Defense Practice Report — 11-dimension weighted readiness score across exactly what hiring managers test: systems design, ML fundamentals, coding, evals, deployment, and more.
• Interview-ready career package — Resume and LinkedIn profile, system design story bank, and a STAR answer library built from the work you finished.
• 92 labs, not passive videos — Every lesson has a quiz, every phase has a hands-on lab, and every project ships with an eval report. You build the muscle memory that shows up in technical interviews.

What you will be able to do

Python · PyTorch · RAG · Agents · Evals · MLOps

Every phase, every lesson, every project

• Setup & Dev Environment (5 lessons) — free — Docker, Git, uv, CUDA, IDE setup, linear algebra, calculus, autodiff, probability, vector search
  • Professional AI Development Environment
  • Linear Algebra for AI: Vectors, Matrices, and Transformations
  • Calculus & Automatic Differentiation from Scratch
  • Probability, Statistics & Information Theory for ML
  • Vector Search from Scratch: TF-IDF, BM25 & Semantic Search
  • Optimization Foundations: Why Training Neural Networks Is Hard
• Python Fundamentals (5 lessons) — free — Variables & control flow, functions & modules, NumPy & pandas, testing & Pydantic, FastAPI & async
  • Python Variables, Types, and Control Flow
  • Python Functions and Modules
  • NumPy & Pandas for AI Engineering
  • Testing & Data Validation with Pydantic
  • FastAPI, CLIs & Async Programming for AI
• SQL & Data Engineering (4 lessons) — SQL foundations, joins & data modeling, window functions, feature engineering & pipelines
  • SQL Foundations for ML
  • Joins & Relational Modeling
  • Window Functions for Features
  • Feature Engineering, Data Quality & Pipelines
• ML Fundamentals (9 lessons) — Supervised vs unsupervised, linear regression, gradient descent, classification, trees & boosting, clustering, reinforcement learning & RLHF, metrics & validation, error analysis
  • What is Machine Learning?
  • Linear Regression from Scratch
  • Gradient Descent & Optimization
  • Classification & Logistic Regression
  • Decision Trees, Random Forests & Gradient Boosting
  • Clustering & Unsupervised Learning
  • Reinforcement Learning: Learning from Reward, Not Labels
  • Metrics & Validation Done Right
  • Error Analysis & Model Cards
  • Bias-Variance Tradeoff & Model Diagnostics
• Deep Learning (7 lessons) — Neural networks from scratch, backpropagation, PyTorch, CNNs, RNNs, training dynamics & regularization, distributed training (DDP, FSDP)
  • Neural Networks from Scratch
  • Backpropagation & Training from Scratch
  • PyTorch Basics: Tensors, Autograd & the Training Loop
  • CNNs for Computer Vision
  • RNNs & Sequence Models
  • Training Dynamics & Regularization
  • Distributed Training: DDP, Gradient Sync, and Multi-GPU
  • Generative Models: Autoencoders, VAEs & GANs
• Transformers (6 lessons) — Attention intuition, attention from scratch, tokenization & embeddings, transformer architecture, pretraining & fine-tuning, hands-on LoRA fine-tuning
  • Attention: Intuition & Visualization
  • The Attention Mechanism from Scratch
  • Tokenization, Embeddings & Positional Encoding
  • The Transformer Block & Full Architecture
  • Pretraining, Fine-tuning & LoRA
  • Hands-On LoRA Fine-tuning
• LLM Engineering (8 lessons) — Model landscape, prompt engineering, structured outputs, context window optimization, multimodal AI, local LLMs & quantization, cost-aware model routing, DSPy prompt optimization
  • The LLM Landscape & Model Selection
  • Prompt Engineering as Interface Design
  • Structured Outputs & Reliable Extraction
  • Context Window Management & Token Economics
  • Multimodal AI & Vision
  • Local LLMs & Open-Weight Models
  • Cost-Aware Model Routing: Smart LLM Dispatch for Production
  • DSPy: Programmatic Prompt Optimization for LLM Applications
  • Context Engineering: Designing the Model's Information Environment
  • Memory Systems for LLM Agents
• RAG Systems (9 lessons) — RAG decision framework, document chunking & ingestion, embeddings & vector stores, hybrid retrieval & reranking, query rewriting, citations & abstention UX, RAG evaluation, production deployment, RAG access control & permissions
  • When RAG Helps, and When It Does Not
  • Chunking, Metadata, and Document Ingestion
  • Retrieval, Reranking, and Hybrid Search
  • Citations, Abstention, and Grounded UX
  • Embeddings & Vector Stores
  • RAG Evaluation & Metrics
  • Advanced Retrieval & Query Rewriting
  • RAG Production Deployment
  • RAG Access Control: Document-Level Permissions in Retrieval Systems
• AI Agents (4 lessons) — Agent loop & tool use, state & memory workflows, human approval & permissions, agent evals & observability
  • The Agent Loop: Tools, Actions, and Observations
  • State, Memory, and Workflow Graphs
  • Human Approval, Tool Permissions, and Sandboxing
  • Agent Evals, Observability, and MCP
• Evaluation & Safety (7 lessons) — Eval fundamentals, red teaming & safety, LLM system evals, RAGAS framework, production observability, responsible AI & fairness, model cards & documentation
  • Evaluation Fundamentals: Beyond Vibes
  • Evaluating LLM Systems: Benchmarks & LLM-as-Judge
  • Red Teaming & AI Safety in Production
  • RAG Evaluation with RAGAS: Automated Metrics for Retrieval & Generation
  • Production Observability: Tracing & Online Evaluation
  • Responsible AI: Bias, Fairness & Ethical Evaluation
  • Model Cards: Documenting ML Models for Transparency and Compliance
  • Guardrails: Input/Output Safety for LLM Applications
• Deployment & LLMOps (6 lessons) — Docker & CI/CD, model serving, optimization & quantization, vLLM serving, MLOps monitoring, shadow deployments & safe rollout
  • Containerization & CI/CD for AI Systems
  • Model Serving & Inference Optimization
  • Model Optimization: Quantization & Distillation
  • Serving LLMs: vLLM, Batching & Throughput
  • MLOps: Monitoring, Drift & Retraining
  • Shadow Deployments: Safe Model Rollout in Production
• Cloud Deployment (4 lessons) — Cloud foundations (AWS/GCP/Azure), deploying models on AWS, data storage & cost, multicloud ops
  • Cloud Foundations for ML Engineers
  • Deploying Models on AWS
  • Data, Storage & Cost on AWS
  • Multi-Cloud Patterns & Cloud Ops
• Career, Portfolio & Capstone Demo Day (8 lessons) — Portfolio building, resume & LinkedIn, system design interviews, STAR stories, interview prep, job search OS, capstone demo, defending AI-assisted work
  • Portfolio & Project Packaging for Hiring
  • Resume & LinkedIn for AI Roles
  • AI System Design Interviews
  • STAR Story Worksheets for AI/ML Interviews
  • Coding, ML Theory & Take-Home Interviews
  • The Job Search Operating System
  • Capstone & Demo Day
  • Defending Your AI-Assisted Work in Interviews
• DSA & Coding Interviews (4 lessons) — Arrays & strings, trees & graphs, dynamic programming, ML coding patterns
  • Arrays & Strings: Two Pointers, Sliding Window & Hash Maps
  • Trees & Graphs: DFS, BFS & Backtracking
  • Dynamic Programming: Memoization & Tabulation
  • ML Coding Under Pressure: numpy, pandas & ML Micro-Tasks

The technologies you will use

PyTorch · OpenAI · AWS · Anthropic · LangChain · RAGAS · Docker · Hugging Face · FastAPI · Qdrant · vLLM · MCP

Roles this course prepares you for

• ML Engineer ($110k–$170k) — Build and deploy ML models — from classical ML to deep learning. You train, evaluate, and ship models that power product features. The most common title in this field.
• AI Engineer ($120k–$180k) — Build LLM-powered applications — RAG chatbots, tool-using agents, structured output pipelines. You design prompts, build retrieval systems, write eval suites, and ship to production.
• LLM Engineer ($130k–$200k) — Specialize in large language model systems — fine-tuning, context window optimization, multimodal AI, local/open-weight deployment. The highest-paying variant of this work.
• Generative AI Engineer ($120k–$185k) — Build production systems with generative models — text generation, RAG, agents, multimodal. Focus on making genAI reliable and safe in real products.
• MLOps Engineer ($120k–$175k) — Own the infrastructure that makes ML work in production — Docker, CI/CD, model serving, monitoring, cost optimization, cloud deployment.
• Applied ML Engineer ($115k–$170k) — Apply ML to specific business problems — recommendation systems, search ranking, fraud detection, content moderation. Common at Amazon (Applied Scientist), Google, Meta.
• AI Product Engineer ($115k–$165k) — Build AI features into products — chatbots, search, recommendations, content generation. You work with PMs to define what to build, then build it end-to-end.
• AI Infrastructure Engineer ($125k–$180k) — Build and maintain the serving layer — vLLM, Triton, model routing, autoscaling, cost optimization, multi-GPU strategies. The infrastructure that makes AI fast and cheap.

What ML & AI engineering actually is

ML & AI engineering is the discipline of building production systems with ML and LLM components. You design prompts, build retrieval pipelines, implement agent workflows, write eval suites, and ship to production. It is not "using ChatGPT" — it is the engineering layer that makes AI reliable, safe, and useful in real products.

What you do every day

You build RAG chatbots that cite sources, agents that use tools safely, structured output pipelines that do not hallucinate, and eval suites that catch regressions before users do. You debug why the model returned something unexpected, optimize token costs, and write post-mortems when things break.

Why companies hire for this

Every company wants AI features. Very few engineers can build them reliably. The gap is not in ML research — it is in production engineering. Companies need people who can take a prototype, make it reliable, evaluate it, and deploy it. That is exactly what this course teaches.

What this course is not

It is not a theory course. You will not derive proofs or read textbooks. It is not a "use AI tools" course. You will not just call the OpenAI API. You will build the systems that make AI work in production — from the data layer to the deployment layer — and you will prove it with a portfolio.

Common questions

How long does the ML & AI Engineering course take?

The full curriculum is 160+ hours of structured content across 14 phases. Most students complete it in 4–6 months at 8–10 hours per week. There's no deadline — you keep access as long as you're subscribed.

What background do I need?

Comfortable with basic Python — loops, functions, classes. That's the only hard prerequisite. Phase 0 teaches the math you need from scratch. No prior ML, AI, or deep learning experience required.

Do I need a GPU or special hardware?

No. All local labs run on CPU. The labs that require GPU compute use cloud-hosted environments so you don't need dedicated hardware. A modern MacBook or any Linux box with 16GB RAM is sufficient.

How current is the curriculum?

The curriculum was last reviewed and updated in June 2026. We update tooling versions and lesson content when major changes happen in the ecosystem.

Can I skip phases I already know?

Yes. Each phase has a prerequisite list and an optional checkpoint quiz. If you pass the checkpoint, you can skip the phase and move on.

What if I get stuck?

You have 24/7 access to a Claude-powered AI mentor that knows your progress and your code. It explains, debugs, and unblocks you without just giving you the answer.

What specific jobs does this course prepare me for?

ML Engineer, AI Engineer, LLM Engineer, Generative AI Engineer, MLOps Engineer, Applied ML Engineer, AI Product Engineer, and AI Infrastructure Engineer. The curriculum covers every skill these roles require.

Key terms in this course

RAG (Retrieval-Augmented Generation) · Agent · Fine-Tuning · Quantization · vLLM · Backpropagation · Chunking · Context Window

Continue your learning path

Agentic AI Engineering · AI Security & Red Teaming · Forward Deployed AI Engineering · Math for AI & ML · Core ML: Algorithms from Scratch · Deep Learning from Scratch · Transformers & LLMs from Scratch · Computer Vision Engineering · NLP & Speech Processing · Generative AI Fundamentals · Reinforcement Learning · Multimodal AI Systems

Start the ML & AI Engineering course

Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.

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