Private Team Training
Train your team on your architecture, not a demo tenant.
Small-cohort AI and data analytics training for 8–12 technical people. On-site or live online, taught by architects who build these systems for a living, using labs we build around your actual stack.
Request a quote
Tell us the shape of it and we’ll come back within one business day with a proposed outline and cost.
Why private, not public
A public course teaches the tool. A private one teaches your system.
When the whole room works at the same company, the training stops being generic on day one, examples come from your data, questions get specific, and nobody has to translate afterward.
Your data, your constraints
We build the labs around your architecture and governance rules, so the examples aren't hypothetical and neither are the answers.
Questions people won't ask publicly
Nobody raises the messy legacy problem in a room full of strangers. In a private cohort, that's usually the most useful hour of the week.
The team levels up together
Shared vocabulary and shared standards across the whole group. Sending three people to a public course rarely changes how a team works.
Scheduled around your delivery
Full days, half days, or two afternoons a week for three weeks. We fit the sprint calendar rather than the other way round.
Content you keep
Lab guides, code, slides, and recordings stay with your team, usable for onboarding the next engineer who joins.
Cheaper past six people
Per-seat public pricing stops making sense around the sixth attendee. At 8–12, a private cohort is usually the less expensive option.
Cohort size
Why we cap it at twelve.
Twelve is the largest group where one instructor can still get to every screen during a lab. Past that, the people who are stuck stay stuck, and the session quietly turns into a lecture.
Below eight, you lose the discussion — the disagreements between a data engineer and an analyst about how something should work are often where the real learning happens.
Larger group? We’ll run parallel cohorts or bring a second instructor rather than stretch one past twelve.
delivery
On-site or live online. Same labs, same instructor.
Both formats run the same hands-on content in browser-based lab environments, nothing to install, nothing to configure on your machines.
on-site
We come to you
Best when the team is co-located and you want the focused, off-the-desk intensity of a room with the door closed.
- Anywhere in the continental US; international by arrangement
- We need a room, a screen, and reliable wifi, that's it
- Attendees work on their own laptops in browser labs
- Travel billed at cost, agreed up front
- Optional architecture working session at the end of the week
Live online
Distributed teams, same room
Live and interactive, not a recording. Best for teams across time zones or when travel budget is the constraint.
- Teams, Zoom, or your platform of choice
- Breakout rooms for paired lab work
- Split into half-days across a week to protect delivery time
- Sessions recorded for anyone who has to miss one
- Instructor available in a shared channel between sessions
Topic menu
What we teach.
Pick one, or tell us the outcome you need and we’ll assemble a custom track. Most engagements end up as a blend.
Most requested
Microsoft Fabric End-to-End
Lakehouse, pipelines, semantic models, and Direct Lake, the full path from raw source to a report someone trusts.
3 days · Data engineers, analysts, BI developers
New
Building Production AI Agents
Go past the prototype: tool use, orchestration, evaluation harnesses, guardrails, and cost control.
2 days · Developers, ML engineers, architects
AI
Microsoft Foundry in Practice
Model selection, prompt management, deployment, and monitoring inside a governed Azure environment.
2 days · Developers, platform engineers
Data
RAG & Enterprise Search That Works
Why retrieval returns the wrong document, and the chunking, hybrid search, and re-ranking that fix it.
2 days · Developers, data engineers
Data
Databricks & the Lakehouse
Delta Lake, Unity Catalog, and production Spark patterns, including the ones that keep your bill sane.
3 days · Data engineers
Analytics
Power BI & Semantic Modeling
DAX that performs, model design that scales, and governance so two teams stop publishing two versions of revenue.
2–3 days · Analysts, BI developers
Engineering
MLOps & Model Lifecycle
Versioning, CI/CD for models, drift monitoring, and rollback, treating models like software you have to operate.
2 days · ML engineers, DevOps
For leaders
AI Literacy for Technical Leaders
What to fund, what to ignore, how to read a vendor claim, and how to evaluate an AI proposal from your own team.
Half day · Directors, VPs, architects
sampler agenda
What three days actually looks like.
Microsoft Fabric End-to-End, delivered to a mixed cohort of data engineers and analysts. Yours will differ, we rescope after the discovery call, but the shape holds.
Microsoft Fabric End-to-End
3 days · 8–12 attendees · On-site or live online
Day 1
Foundations & ingestion
09:00
Foundations & ingestion
OneLake, workspaces, capacities, and how the pieces relate to what you already run.
11:00
Lakehouse vs warehouse Lab
Build both, load the same data, and compare what each is good at.
13:30
Ingestion patterns Lab
Pipelines, Dataflows Gen2, and shortcuts, with the tradeoffs that decide which to use.
15:00
Medallion architecture in practice Lab
Bronze to silver, incremental loads, and handling late-arriving data.
16:15
Open floor
Your environment, your questions. Usually the most valuable half hour of day one.
Day 2
Transformation & modeling
09:00
Notebooks and Spark in FabricLab
PySpark transformations, when notebooks beat pipelines, and cost implications..
11:00
Gold layer & semantic models Lab
Star schema design, relationships, and the modeling decisions you can’t undo cheaply.
13:30
DAX that performs Lab
Measures, context transition, and diagnosing a slow visual with Performance Analyzer..
15:30
Direct Lake
How it actually works, when it falls back to DirectQuery, and how to keep it from doing so.
Day 3
Production, governance & AI
09:00
Deployment pipelines & source control Lab
Dev/test/prod in Fabric, Git integration, and what still has to be done by hand.
11:00
Governance & security Lab
Workspace roles, row-level security, sensitivity labels, and lineage.
13:30
Capacity, cost & performance
Reading the capacity metrics app, finding what’s burning CUs, and fixing it.
15:00
AI on your data Lab
Copilot, AI functions, and grounding a model on Fabric data, with the governance caveats.
16:15
Architecture working session
We whiteboard your real platform and leave you with a written set of recommendations.
How it works
From request to delivery in four steps.
01
Discovery call
Thirty minutes on your team’s current level, your stack, and what they should be able to do afterward.
02
Proposed outline
A written agenda, lab plan, and fixed cost, usually within one business day of the call.
03
We build the labs
Environments provisioned and content tailored to your architecture before anyone walks in.
04
Delivery & follow-up
Training runs, materials stay with your team, and we check in a month later on what stuck.
★★★★★
"This is THE best course I have taken so far. The instructor really knows the tool and is the most interesting, passionate instructor I have heard to date."
Nancy McGuire · Course attendee
questions
Before you ask for a quote.
What does it cost?
It depends on topic, length, format, and how much lab customization you want. Send the form and you’ll get a fixed price with the proposed outline, no hourly surprises. For most teams of 8–12, a private cohort works out cheaper than sending the same people to public courses.
What if our team has mixed skill levels?
Common, and workable. We send a short pre-assessment before the engagement and adjust pacing and lab difficulty from the results. Paired lab work also helps, stronger attendees consolidate their own understanding by explaining it.
Do you need access to our environment?
Not necessarily. We provide sandboxed lab environments by default, so nothing touches your systems. If you’d rather train in your own tenant with your own data, we can do that instead, it makes the training more directly applicable but takes more setup on your side.
Can we split it across several weeks?
Yes, and for online delivery we often recommend it. Four half-days across two weeks usually retains better than two full consecutive days, and it doesn’t take the whole team out of delivery at once.
What do attendees need?
A laptop and a browser. Labs run in hosted environments, no installs, no admin rights, no VM downloads eating the first ninety minutes.
Can you train more than twelve people?
Yes, as parallel cohorts or with a second instructor. We don’t run a single instructor past twelve because lab support is what makes the format work.
What happens after the training?
Your team keeps the materials, lab guides, and recordings. We follow up about a month later to see what’s landed and what’s stalled. Teams that want ongoing support usually move to a fractional architect arrangement.
Tell us what your team needs to learn.
Send the shape of it, size, topic, format, timeframe, and you’ll have a proposed outline and a fixed price within one business day.