Finally connect the dots from all those tutorials. A hands-on, self-paced course for developers and data professionals on how top AI teams design, build, and ship production-ready AI systems.
AI changes quickly, and most courses stay too close to tutorials.
A structured path through the systems used in real AI projects.
Hi, I'm Dave, an AI engineer with over a decade of experience and both a BSc and MSc in AI from VU Amsterdam. I run Datalumina, where my team and I have shipped 50+ production AI systems for B2B clients across SaaS, finance, logistics, and developer tools, including work for teams like TimescaleDB, ClickUp, and n8n.
The GenAI Accelerator is built from that project experience. It focuses on the architecture, tools, and engineering decisions that determine whether an AI system works for real users, real data, and real business constraints.
This AI engineering course walks through the process Datalumina uses to move from client brief to deployed AI system. You will work with the templates, architecture patterns, and deployment workflows behind real production projects.
By week 6, you will understand how the pieces fit together: Python, APIs, RAG, background workers, evals, monitoring, deployment, and the engineering judgment needed to ship reliable AI applications.
Understand the fundamentals of Python for AI Engineering and modern LLM systems. Learn advanced prompt engineering and set up a production-grade environment using uv. Work with the core tools behind enterprise AI platforms built for scale, privacy, and performance.
Structure AI projects for reliability and scale. Work with Pydantic to build type-safe systems and manage data across complex LLM workflows. Apply context engineering and design frameworks used by leading AI teams to create modular architectures that make debugging, testing, and scaling possible.
Set up the backend infrastructure used in production-grade AI systems. Learn to work with FastAPI, Celery, Redis, Docker, MCP, PostgreSQL, and Alembic to run secure, scalable AI workflows. Understand how these components fit together to support real-world LLM applications with reliability and speed.
Build complete RAG pipelines from scratch that ground AI responses in real data. Turn unstructured information into vectors and connect your LLMs to external knowledge sources for higher accuracy. Work with vector databases, hybrid search, and advanced algorithms to optimize retrieval performance.
Track every LLM trace in granular detail to understand exactly how your AI system behaves. Use Langfuse for monitoring and debugging performance at every step. Add guardrails that ensure reliable, safe outputs and build evaluation pipelines (evals) for continuous improvement across your AI stack.
Deploy your AI system using FastAPI, Docker, and modern cloud best practices. Set up a VPS, manage SSL certificates with Caddy, and build CI/CD pipelines for automated deployment. Implement application tracking with Sentry and follow security principles that keep your AI systems safe in production.
I joined this course based on Dave's excellent YouTube content. And I can only say that my expectations of this outstanding course were exceeded many times over. This here deserves my utmost respect.
I'm confident that you'll love this course. But if it's not the right fit, no worries! Get a refund within 30 days.
Prior to this course, integrating AI into a project was scatter shot. I went from 'Pretty sure we can do that' to 'I know how to do that'. If you're on the fence, dive in. The value is vast and the lifetime access is no joke!
To anyone considering this program: it's more than an investment, it's a gift. We're getting tremendous value for the money.
It gave me the knowledge and confidence to provide my customers with systems they can trust, systems that meet their security requirements and work predictably in production environments.
Even with 18 years in software engineering, I found immense value. You learn the why behind every architectural decision, not just the how.
The detailed level of instruction has given me the confidence to begin designing and implementing AI solutions for a variety of finance and education use cases.
Very few courses cover the complete end-to-end AI engineering lifecycle, and this is definitely one of them. It's built around proven engineering principles and practices, with a focus on designing scalable AI applications.
In a world full of half-baked YouTube reviews and amateurs posing as experts, Datalumina managed to stand out head and shoulders above the rest. They took me from pieced-together information to a confident AI engineer.
Enroll once and get the full program: lessons, labs, community, and resources.
Work through the weekly modules in order, or focus on the systems you need most.
Apply the patterns from the course to build and deploy a production-ready AI application.
