Evaluate the bias, fairness and explainability obligations attaching to machine learning model development.
Strategies for Building and Managing Machine Learning Models
Develop the judgement and the documentation needed to run machine learning model development properly.
📍 Tripoli🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
The constraint on machine learning model development is rarely the model or the platform — it is the data and the operating discipline behind it. Boards are asking for measurable returns from model development, not demonstrations. Every module pairs a short input on this aspect of digital and data-driven work with structured practice on the participant's own material. The programme is built to be used, and every section of machine learning model development it covers ends in something applicable. They leave able to brief senior management on the digital and data-driven work capability in terms that support a decision. Post-incident reviews keep identifying weaknesses in model development that were visible long before the incident. The difficulty is not agreeing that machine learning model development matters — it is deciding what to stop doing to make room for it. The content is relevant to those who own model development and to those who are held accountable for its results. The final module sets out how progress on the wider digital and data-driven work agenda will be evidenced six months later.
Expected Learning Outcomes
Set the minimum documentation for model development that is genuinely necessary, and stop there.
Set retention, lineage and deletion rules for the data flowing through machine learning model development.
Prepare a short, evidence-based briefing on model development for senior management.
Build the internal capability for machine learning model development rather than depending on external support indefinitely.
Define success criteria for model development in business terms before any technology is selected.
Map the regulatory obligations that apply to machine learning model development in each market of operation.
Who Should Attend
Managers of multi-site operations seeking consistency in machine learning model development.
Business analysts translating requirements for model development.
Information security officers reviewing the exposure created by machine learning model development.
Programme managers coordinating delivery of model development across teams.
Coordinators responsible for keeping records and documentation of machine learning model development current.
Solution architects designing how model development fits the existing estate.
Course Modules
Machine learning model development: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1Escalation and decision rights in machine learning model development
- Check that records of machine learning model development answer the questions likely to be asked.
- Define the service level model development must meet and what happens when it is missed.
- Decide which legacy process machine learning model development retires, and set the date.
- Name a single owner for each element of model development.
Session 2Comparing model development with recognised practice
- Agree who is on call for machine learning model development outside working hours.
- Design the pilot for model development so that a negative result is still useful.
- Collect evidence on the present handling of machine learning model development before proposing changes.
- Record the reasoning behind each architectural choice in model development.
Model development: vendor selection and avoiding lock-in
2 sessions · 8 pointsSession 1The pilot that actually settles the argument about model development
- Test the procedure for machine learning model development against a realistic scenario.
- Verify six months later that changes to model development have held.
- Arrange the handover of machine learning model development so capability survives staff changes.
- Establish who is informed, consulted and accountable in model development.
Session 2The governance model development needs and the governance it does not
- Measure the current quality of the data feeding machine learning model development before assuming it is usable.
- Agree what will be standardised in model development and what will not.
- Confirm that contractual obligations around machine learning model development are understood.
- Plan how model development is versioned and how a bad release is rolled back.
Model development: architecture, integration and the existing estate
2 sessions · 8 pointsSession 1Making model development work when resources are constrained
- Estimate compute and licensing cost for machine learning model development at expected and at peak load.
- Classify the data in model development and apply access controls that match the classification.
- Plan the sequence in which improvements to machine learning model development will be introduced.
- Anticipate the objections model development will raise and prepare the answers.
Session 2Making machine learning model development secure without making it unusable
- Build the user briefing that explains what machine learning model development does and does not decide.
- Set the metrics that will show whether model development is drifting from its intended behaviour.
- Confirm the retention and deletion rules applied to data inside machine learning model development.
- Map the handovers in model development between functions and secure them.
Model development: people, skills and the change that follows
2 sessions · 8 pointsSession 1Reading the true cost of running model development
- Establish the boundary of machine learning model development and record what sits outside it.
- Verify that model development still performs when input volume doubles unexpectedly.
- Prepare the summary of machine learning model development that senior management will read.
- Identify every system model development must read from or write to.
Session 2Where model development touches systems nobody wants to change
- Identify single points of dependency in machine learning model development and reduce them.
- Check that model development still works when volumes rise unexpectedly.
- Assess the regulatory obligations machine learning model development triggers in each jurisdiction.
- Specify the fallback path when model development is unavailable.
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Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
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