Executive AI
An AI literacy track for leaders shaping AI strategy. Connect AI fluency with opportunity assessment, investment priorities, responsible ownership, and organizational adoption.
Explore this serviceAI Training / Applied machine learningFrom model concepts
From model concepts
to production thinking.
An applied AI literacy track for data scientists building production systems. Connect data preparation, model development, evaluation, and operational considerations through a representative use case.
Who it is for
A practical starting point.
Data scientists and analytics practitioners with a working foundation in data and programming.
The challenge
A model experiment is only one part of a usable system. Practitioners need to understand how data choices, evaluation design, and operational requirements shape the result.
Inside the engagement
What your team will explore.
01
Frame the prediction problem
Translate the business question into a modeling task. Define the target, intended decision, baseline, and constraints before working on model selection.
02
Prepare and inspect data
Explore data quality, availability, and preparation choices. Discuss how missing information and unsuitable data can limit the result.
03
Develop and evaluate
Work through model development and comparison using criteria relevant to the use case. Examine errors and discuss what the evaluation does and does not establish.
04
Plan production use
Connect the model to a wider workflow. Consider deployment, monitoring, ownership, and how the team will review future changes.
Learning outcomes
Turn learning into practice.
We agree the scope and outputs around your starting point. A typical engagement can include:
- A worked modeling problem and baseline
- A practical data-preparation exercise
- An evaluation comparison and error review
- A production-readiness discussion for the use case
In context
Technical exercise: compare against a baseline
Participants build a model for a representative task, compare it with an agreed baseline, and inspect where it fails. They then identify the additional work required before the result could support a real workflow.
Getting started
Shape the program around your team.
Tell us who will attend, their current experience, and what they should be able to do afterwards. We shape the exercises and depth around that outcome.
Prerequisites, tooling, and example data are agreed before the program.
Plan this engagementExplore AI Training
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