Live Cohort Course
AI Teams That Ship
A 6-week intensive for technical leaders who want to structure, hire for, and lead AI teams that actually ship to production.
Sound familiar?
You hired expensive AI talent that doesn't work together as a team. Research scientists who can't ship code, AI engineers who don't understand the business, and nobody owns production.
You built impressive prototypes that never make it to production. The demo looks great, stakeholders are excited, then it sits for six months while you figure out deployment, monitoring, and scale.
You made infrastructure decisions you regret. Built when you should have bought. Bought when you should have built. Now you're stuck with technical debt and mounting costs.
Your projects drag on without clear shipping milestones. Agile doesn't work for AI. Waterfall doesn't either. Your team is stuck in endless iteration with no production deadline in sight.
You struggled to grow from 3 to 10 to 20 engineers. Your first few hires were great, but as you scale, coordination breaks down, culture dilutes, and shipping slows.
6 weeks, 6 core decisions
Each week tackles one decision that determines whether your AI team ships or stalls.
Week 1
Structure & Strategy
The foundation for how to organize your AI team for shipping. Covers centralized vs. embedded team models, research scientists vs. AI engineers vs. MLOps roles, how to size your team for your goals, and when to hire which roles.
Deliverable: Your AI team org chart and hiring plan
Week 2
Hiring
Building a team that ships, not just experiments. Learn how to write job descriptions that attract shipping-focused candidates, interview for AI/ML roles across technical and cultural fit, distinguish research mindset from engineering mindset, and evaluate candidates in the age of LLM-assisted coding.
Deliverable: Your hiring playbook for AI roles
Week 3
Infrastructure & Onboarding
Getting new hires productive fast and making smart tooling decisions. Covers the build vs. buy framework for AI/ML tooling, model serving and deployment options, eval systems, and documentation that actually gets used.
Deliverable: Your 30-day onboarding plan and infrastructure roadmap
Week 4
Prototype to Production
The hardest transition in AI — from working demo to production system. Learn to bridge the research-to-engineering gap, manage uncertainty and iteration cycles, define production-ready for AI systems with evals, and know when to kill projects vs. double down.
Deliverable: Your production readiness checklist
Week 5
Shipping Cadence & Culture
How to actually ship AI projects on a predictable schedule. Covers why traditional agile and scrum fail for AI projects, alternative processes that work for ML teams, managing stakeholder expectations around AI uncertainty, and balancing exploration vs. execution.
Deliverable: Your AI project management framework and culture playbook
Week 6
Scaling & Custom Q&A
Growing from 5 to 20 engineers and addressing your specific challenges. Covers scaling team structure and communication, budget and resource allocation, technical roadmapping, cost management at scale, and when to add management layers. Plus an open Q&A session focused on your challenges.
Deliverable: Your 6-month AI team growth plan
What's included
- 6 weekly live sessions (90 min each)
- Session recordings available within 24 hours
- Private Chat community
- Weekly office hours (45 min, group)
- Templates and frameworks for every week
- Lifetime access to all recordings and materials
Schedule
4-6 hours per week
All sessions are recorded. Can't make a session? Watch the recording within 24 hours.
Who this course is for
- VPs and Directors building their first AI team who know the business need but want guidance on structure, hiring, and process
- CTOs adding AI capabilities to existing engineering teams where the engineering culture is strong but AI/ML is new territory
- Senior ICs promoted to lead AI initiatives who went from individual contributor to team lead and want to level up their leadership skills
- Technical founders building AI startups who need to scale the team beyond just themselves
- Engineering leaders who've made expensive AI hiring mistakes and need a proven framework to get it right
Dan Gerlanc
Dan co-founded .txt in 2023, then raised $11.9M in 2024 to solve one of the hardest problems in AI: making LLMs reliable through structured generation.
Before .txt, Dan spent over 15 years building and leading technical teams. As VP of Engineering at Normal Computing, he led engineering at a deep-tech AI startup focused on production-ready generative AI workflows. As Sr. Director at Ampersand, he built and led data science and ML engineering teams of 8+ engineers for 5 years, where the team’s forecasts were used to guide over $1 billion in ad spend. He founded Enplus Advisors (rated 4.8/5 by Clutch.co) and worked as a Quantitative Analyst at Geode Capital Management building models managing billions in assets.
On O’Reilly Media, Dan has taught thousands of students across courses on Python, Pandas, and Dask, earning an 8.56/10 rating from 104 student feedback surveys.
What people are saying
"As a member of several of Dan's teams I've witnessed his ability to recruit and manage top talent to deliver on complex technical projects. His logical, no-nonsense approach to managing product and project plans makes him a pleasure to work with."
Jeffrey Enos
SVP, Head of Portfolio Tools Engineering, Acadian Asset Management
"Dan was both my manager and a mentor as I transitioned from data science to software engineering. He combines a deep understanding of the technical aspects of system design with an ability to explain the real-world tradeoffs of decisions, which accelerated my learning."
Aaron Schwartz, PhD
Software Engineer, .txt
"I thought the training was excellent in all respects. I especially appreciated the informal Q&A that ran through the whole thing, where your expertise shone through and your relaxed, informal style made everyone comfortable asking the simplest questions."
Colm O'Cinneide, PhD
Professor of Mathematics, Columbia University
Questions
Is this technical or managerial?
Both. You'll learn team structure, hiring, and culture (managerial) AND infrastructure decisions, development environments, and technical processes (technical). The best AI leaders understand both.
Do I need prior AI/ML experience?
Yes. This program assumes you understand ML basics — what models are, training vs. inference, and so on. We're teaching how to build teams and ship to production, not ML fundamentals.
What if I can't attend the live sessions?
All sessions are recorded and available within 24 hours. But I strongly encourage live attendance — the Q&A and discussion are valuable.
Can my company pay for this?
Absolutely. Most participants use their company's L&D budget. We provide proper invoices. The program also qualifies for professional development hours.
What's the time commitment?
Plan for 4-6 hours per week: 90 minutes for the live session on Tuesdays, 45 minutes for office hours on Thursdays, and 1 - 2 hours for templates and homework.
Will I get personalized feedback?
Yes, during office hours and in the community.
Is this recorded or live?
Live cohort-based learning with recordings available. The value is in the live interaction, cohort community, and timely feedback — not just watching videos.
What if my team is already 20+ people?
The program covers scaling from 0 to 20. If you're already at 20, Week 6 focuses on your stage. You'll still get value from the infrastructure, process, and culture weeks — many large teams struggle with these fundamentals.
Do you offer corporate workshops?
Yes. For companies wanting customized training for their leadership team, I offer 2-day workshops. Email for details.
What's your refund policy?
Full refund if you attend Week 1 and decide it's not right for you. You must request your refund within 48 hours of the end of the Week 1 session. No refunds after that.
Will there be future cohorts?
Yes. I plan to run 4-5 cohorts per year. But each cohort is limited to 25 people, so don't wait if you're ready now.
Ready to start?
A 6-week intensive for technical leaders who want to structure, hire for, and lead AI teams that actually ship to production.