Building AI Applications for Data Scientists and Software Engineers
Hugo Bowne-Anderson
AI Builder/Educator (6 Million Students)
Stefan Krawczyk
AI Builder with 15 years of experience
Master the patterns and processes that underpin successful GenAI app development
If you’re a software engineer or data scientist, chances are you’ve built an impressive proof of concept (POC) that showcases the potential of LLMs.
But these POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.”
It doesn’t have to be this way.
With the right principles, processes, & workflows, you can build LLM applications that work in production, without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready.
You'll learn how to build with state-of-the-art tools like RAG, fine-tuning, MCP, context enginerring, and agents, but the emphasis is on building scalable, robust systems, ensuring you leave with the mindset and skills to adapt as the technology evolves.
You’ll not only learn how to design, test, & deploy generative AI applications, but also how to iterate and improve rapidly.
Join 300+ engineers from Meta, Netflix, Amazon, and more to create AI systems that solve real problems, deliver real value, & scale with confidence.
What you’ll learn
Build AI-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more
You’ve seen impressive generative AI demos, but how do you take them to production?
You’ll learn the exact workflows, tools, and engineering practices needed to turn concepts into reliable, scalable applications.
Learn everything you need to build robust and reliable agentic systems.
Incorporate tool-calling, memory and retrieval to augmented LLM workflows and beyond.
Stop wasting time in endless iteration cycles.
Through guided projects, you’ll discover how to set up workflows that refine your applications faster and with less frustration.
Learn how to use "Evals" and LLM Judges to guide your software development lifecycle.
Tired of theoretical knowledge?
You’ll build a fully functional app that uses text and image models to query PDFs.
You'll gain practical experience solving complex real-world problems.
Leave the course with a project you can use to showcase your ability to deploy reliable AI systems.
Who this course is for
Data scientists, machine learning engineers who are sick & tired of seeing and building prototypes & want to ship reliable LLM applications
Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.
What's included
Live sessions
Learn directly from Hugo Bowne-Anderson & Stefan Krawczyk in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
4+ Office Hours and Builders Clubs
Extra sessions with instructors and builders from previous cohorts: deep dives, debugging help, and personalized feedback.
12 Hands-On Python Notebooks
Ready-to-run notebooks and scripts so you can learn and practice every concept immediately.
$500+ in Cloud & AI Credits
Test compute platforms, vector dbs, observability platforms, LLM APIs, and infra with over $500 in partner credits
Community of Builders
Build with like-minded people previous cohorts have seen students from Netflix, Meta, The United Nations, Amazon, and more.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Lifetime Discord Community
Private discord for peer reviews, job leads, and ongoing support forever.
Expert Guest Speaker Program
Curated talks from builders at the forefront of AI software development and tool-building.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
9 live sessions • 8 lessons
Week 1
Mar
9
Optional: Onboarding Session for Building AI Applications
Foundations of LLM Software Development
Mar
9
Foundations of LLM Software Development
Mar
11
LLM APIs, Prompt Design, & Reliable Outputs
Week 2
Iteration, Evaluation, and Observability
Mar
16
AI Evals and Software Feedback Loops
Mar
18
Observability and Debugging in Development and Production
Free resource
Mastering LLM Application Testing
Write effective pytest cases for testing LLM outputs.
Build a framework for iterative improvement of LLM apps
Evaluate LLM results systematically to improve stability
Schedule
Live sessions
4 hrs / week
Mon, Mar 9
10:00 PM—10:45 PM (UTC)
Mon, Mar 9
11:00 PM—1:00 AM (UTC)
Wed, Mar 11
11:00 PM—1:00 AM (UTC)
Testimonials
- Stefan & Hugo are two of the best educators in this space. You'll learn a lot from their practical and pragmatic insights to building reliable AI.
Elijah ben Izzy
Co-founder & CTO DAGWorks Inc. - I want to take this course.
Hamel Husain
AI Engineer, Parlance Labs - Hugo is amazing. As a physician in NYC learning DataScience, I had Hugo as my DataCamp professor. He is articulate, passionate about Python, and made me feel like a true “pythonista.” His deep knowledge and ability to help new students are unmatched. I’ll end as I began: Hugo is simply amazing.
Richard Savel
Chair, Dept of Medicine, Jersey City Medical Center - Hugo Bowne-Anderson is a thought surgeon."
Cassie Kozyrkov
CEO, Google's first Chief Decision Scientist, AI Adviser, Decision Strategist, Keynote Speaker (makecassietalk.com), LinkedIn Top Voice - Hugo’s legendary teaching launched my data science career—his GenAI course is bound to set a new standard in the field.
Krystyna Perez
Data Scientist | Telecommunications
Frequently asked questions
$2,100
USD