AI-powered engineering, automation, and intelligent systems for developers, IT, and technical leaders. Build, deploy, and operate AI-native software at the scale and reliability of mission-critical workloads — taught by operators who have shipped AI inside the world's largest platforms.
Backend, full-stack, and ML engineers building AI features users depend on every day.
Infrastructure leaders standing up AI platforms with the reliability discipline of mission-critical workloads.
Heads of engineering and CTOs deciding what AI capabilities to build, buy, or run internally.
Senior architects designing the next generation of AI-native systems for the global ten thousand enterprises.
Most AI features ship as demos and break on contact with real users. This course teaches the engineering discipline that turns LLM calls into production-grade systems — LLM APIs and SDKs, embeddings and retrieval (RAG), prompt engineering for production, evals frameworks, observability, and the cost and latency basics every team gets wrong on the first try. You'll leave with a working AI feature deployed to a real environment and the playbook to ship the next ten.
Agent demos are easy. Agents that hold up under real load, real edge cases, and real budget constraints are hard. This course teaches the engineering patterns behind production agents — agent loops, tool use, internal copilots, customer-facing agents, memory and state, guardrails, and evaluation. You'll ship a real agent end-to-end, instrument it, and operate it under the same reliability bar as any other mission-critical workload.
Every team in the company wants to ship AI. Without a platform, they each ship their own broken version of the same thing. This course teaches you to design and operate the AI platform layer the global ten thousand enterprises are building right now — model gateways, observability, cost controls, security, governance, on-call playbooks, and capacity planning for AI workloads. You'll leave with a platform blueprint your CTO can take to the board.
Master the engineering primitives behind every production AI system — LLM APIs, RAG, evals, and observability. Ship a real feature, not a demo.
➔Design, build, and operate agents and automations that survive real users. Tool use, memory, guardrails, and evals — in production, not in a notebook.
➔Stand up the AI platform layer for your enterprise. Gateways, observability, cost controls, governance — the boring parts that decide who scales.
A real AI feature deployed to a real environment, with evals, observability, and the SLO discipline of any other mission-critical workload.
A production agent with tool use, memory, and guardrails — integrated with real systems and operated under real reliability constraints.
A reference architecture for your enterprise's AI platform layer — gateway, observability, governance — ready for board-level review.
A working evaluation harness, golden datasets, and the guardrail patterns that keep AI systems honest as they scale.
End-to-end tracing, cost accounting, and quality SLOs across every model call — instrumented to the standard of mission-critical workloads.
Complete all 3 courses + capstone to earn the AI Impact Foundation Technical Track Professional Certification.
AI-Native Engineering Foundations. Ship your first production-grade AI feature.
Agents, Automation & Tooling. Operate agents that survive real users.
AI Platform & Operations. Stand up the platform your enterprise runs on.
All 3 courses + AI Impact Foundation Certification + 6-month community access. Save $491 vs. buying separately.
All 3 courses run as a live cohort with instructor feedback, code reviews with operators who have shipped AI at scale, and peer accountability through every project.
Founding member pricing: First 200 enrollees get an additional 30% off. Join the waitlist to reserve your spot.
No. This track is built for engineers, not ML researchers. If you can read and write code in any modern backend or full-stack language, you have what you need. We focus on the production engineering around AI — APIs, evals, observability, agents, and platform — rather than model training or research-grade ML.
Python is primary for examples and labs, with TypeScript on the application side via the Vercel AI SDK. The concepts — RAG, evals, agent loops, gateways, observability — are language-agnostic. Engineers from Go, Java, Rust, and C# backgrounds report the patterns transfer directly to their stack.
Yes. Each course is designed to stand alone while also building toward the full track outcome. Many engineers take Course 1 to ship their first production AI feature, then continue with Agents or Platform once they hit the next problem. The bundle discount applies if you upgrade later within 90 days of your first enrollment.
Plan for 3 intensive days per course — roughly 6–8 hours per day. Each day blends short video lessons, hands-on labs, and live workshops or code reviews. The format is built for working engineers who want a sharp, focused sprint rather than a slow weekly cadence stretched across months.
Yes. Every course has a real deployment requirement. Course 1 ships an AI feature to a real environment with evals and observability. Course 2 ships a production agent end-to-end. Course 3 produces a platform blueprint reviewed by operators who have shipped AI at scale. These are working artifacts, not slideware.
Yes. Every professional course enrollment directly funds a full scholarship for an underserved student through the AI Impact Foundation (501(c)(3)). It's not a separate donation — it's built into the model. You learn AI. A student gets access to AI education, a laptop, mentorship, and meals. One enrollment, two futures.
Sign up for the newsletter to follow along, or join the waitlist to lock in founding-member pricing. Every enrollment funds a student scholarship.
No spam. Just a heads-up when we launch.