01
You already have a baseline
The program assumes you have used at least one cloud service before — even a personal project counts. Total beginners will find the pace uncomfortable in the first two weeks.
Cloud Platforms for AI — Fresh Notebook
Where do you actually run your AI work?
Most practitioners know the theory. The harder question is knowing which cloud platform fits the problem — and being able to set it up without guessing. That is what this program addresses directly.
Hands-on exercises across AWS, GCP, and Azure — not slides, actual deployments.
Each assignment is scoped to a real infrastructure decision you will face in practice.
Participants from across Canada work through the same material at their own pace.
What stays with you six months later
The ability to read a cloud cost estimate and know immediately what is driving it — that kind of fluency does not come from a tutorial. It comes from having made decisions under realistic constraints and seen what happened.
Participants consistently report that the biggest shift is not in what they know but in how quickly they can orient themselves in an unfamiliar cloud environment. That speed compounds over time.
When a team moves from one provider to another, the person who has worked across platforms is the one who unblocks everyone else. That position is worth building toward deliberately.
The assignments here are designed so that the reasoning behind each choice is visible — not just the correct answer. That reasoning is what transfers to the next job, the next project, the next provider.
This works well under specific conditions
02
You can commit four to six hours weekly
The assignments are cumulative. Skipping a week does not just create a gap — it makes the next assignment harder to complete. Consistent time matters more than total hours.
03
You are comfortable with ambiguity
Real cloud infrastructure problems rarely have one correct answer. The exercises are designed to surface that ambiguity deliberately. If you need a single right answer, some sessions will feel frustrating.
04
You are working toward a specific goal
Participants who have a concrete reason — a role they want, a project they are building — tend to get more from the exercises than those exploring generally. The material rewards applied intent.
05
Location is not a barrier here
The program runs fully online and is structured for participants across different time zones within Canada. Live sessions are recorded, and the collaborative tools work asynchronously.
06
You are willing to share your work
Peer review is built into the structure. Other participants will see your approach and give feedback. That exposure is part of how the learning works — not optional.
The distance between knowing and doing
Most people who apply have read documentation, watched videos, and understood the concepts. The gap is not knowledge — it is the moment when you have to make a decision with real consequences and no one to confirm you are right.
That gap is exactly what the workshop format is designed to close. Not by simulating it — by putting you in it with enough support that you can work through it rather than around it.
See upcoming program datesYou can describe how a managed ML service works. You have not yet decided whether to use it or build the pipeline yourself — and defended that choice to a team.
You understand IAM roles in principle. You have not yet debugged a permission error at 11pm when a deployment is blocked and the documentation is not helping.
You know the cost models exist. You have not yet looked at a $400 bill and traced exactly which resource caused it and how to prevent it next time.
You have followed a tutorial to completion. You have not yet started from a blank environment and made every structural decision yourself.
The program in numbers
14
workshop sessions per cohort, each built around a distinct infrastructure decision
3
cloud providers covered in depth — AWS, Google Cloud, and Azure — with comparative exercises
8
collaborative assignments requiring peer review and written rationale, not just a working result
12
regions across Canada with active participants in current and recent cohorts
What carries forward after the last session
The certificate matters less than the habit of thought it represents. Participants who have worked through fourteen sessions of real decisions tend to approach new cloud environments differently — with a structured curiosity rather than anxiety.
Access to the exercise archive and peer network remains open after the cohort ends. The problems stay available because revisiting them at a different skill level reveals things that were not visible the first time.
Fresh Notebook has been running cohorts since 2022, and the most consistent feedback is not about the content — it is about the confidence that comes from having been uncertain and worked through it anyway. That is the thing that transfers to the next role.
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