Set retention, lineage and deletion rules for the data flowing through adaptive AI and continuous learning systems.
Adaptive Artificial Intelligence and Continuous Learning Systems
Develop the judgement and the documentation needed to run adaptive AI and continuous learning systems properly.
📍 Tripoli🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Boards are asking for measurable returns from adaptive AI and continuous learning systems, not demonstrations. Technology choices around learning systems are easy to reverse on paper and expensive to reverse in practice. The course sets out a working method for this aspect of digital and data-driven work that participants can apply the week they return. Participants develop a defensible line of reasoning for the choices they make about adaptive AI and continuous learning systems. Benchmarking exercises repeatedly place the practice within digital and data-driven work among the areas with the widest performance spread. The programme works equally well for those formalising learning systems for the first time and those improving an existing approach. The difficulty is not agreeing that adaptive AI and continuous learning systems matters — it is deciding what to stop doing to make room for it. Cases are chosen to expose the trade-offs in learning systems rather than to illustrate ideal conditions. The course ends by identifying what the participant will stop doing to make this aspect of digital and data-driven work sustainable.
Expected Learning Outcomes
Prepare a short, evidence-based briefing on learning systems for senior management.
Anticipate the objections that adaptive AI and continuous learning systems will attract internally and answer them in advance.
Evaluate the bias, fairness and explainability obligations attaching to learning systems.
Specify the data adaptive AI and continuous learning systems depends on, where it originates and who is accountable for its quality.
Define escalation and fallback for learning systems when the automated path fails.
Handle the trade-offs in adaptive AI and continuous learning systems between speed, cost and assurance explicitly rather than implicitly.
Who Should Attend
Vendor and contract managers overseeing suppliers involved in adaptive AI and continuous learning systems.
Operations managers whose processes are changed by learning systems.
Coordinators responsible for keeping records and documentation of adaptive AI and continuous learning systems current.
Quality staff verifying that learning systems performs as designed.
Programme managers coordinating delivery of adaptive AI and continuous learning systems across teams.
Information security officers reviewing the exposure created by learning systems.
Course Modules
Adaptive AI and continuous learning systems: cost, licensing and total running expense
2 sessions · 8 pointsSession 1Keeping adaptive AI and continuous learning systems alive after the initial push
- Test adaptive AI and continuous learning systems against edge cases drawn from real historical records.
- Write down the assumptions underpinning the approach to learning systems.
- Build the user briefing that explains what adaptive AI and continuous learning systems does and does not decide.
- Prepare the response for the most likely failure in learning systems.
Session 2The pilot that actually settles the argument about learning systems
- Agree who is on call for adaptive AI and continuous learning systems outside working hours.
- Collect evidence on the present handling of learning systems before proposing changes.
- Identify every system adaptive AI and continuous learning systems must read from or write to.
- List the data sources learning systems consumes and confirm each has a named owner.
Learning systems: people, skills and the change that follows
2 sessions · 8 pointsSession 1Proving learning systems paid for itself
- Decide which legacy process adaptive AI and continuous learning systems retires, and set the date.
- Set the metrics that will show whether learning systems is drifting from its intended behaviour.
- Draft the minimum viable delivery roadmap for adaptive AI and continuous learning systems.
- Set out the decisions in learning systems that require sign-off and by whom.
Session 2Where learning systems touches systems nobody wants to change
- Review whether adaptive AI and continuous learning systems is aligned with the objectives of the transformation programme.
- Verify that learning systems still performs when input volume doubles unexpectedly.
- Design the pilot for adaptive AI and continuous learning systems so that a negative result is still useful.
- Identify single points of dependency in learning systems and reduce them.
Learning systems: architecture, integration and the existing estate
2 sessions · 8 pointsSession 1Where learning systems typically breaks, and why
- Define the service level adaptive AI and continuous learning systems must meet and what happens when it is missed.
- Benchmark the organisation's learning systems against comparable operations.
- Map the handovers in adaptive AI and continuous learning systems between functions and secure them.
- Measure the current quality of the data feeding learning systems before assuming it is usable.
Session 2Sizing adaptive AI and continuous learning systems honestly before committing budget
- Check that adaptive AI and continuous learning systems still works when volumes rise unexpectedly.
- Identify the skills the team lacks to operate learning systems independently.
- Assess the regulatory obligations adaptive AI and continuous learning systems triggers in each jurisdiction.
- Establish the boundary of learning systems and record what sits outside it.
Learning systems: from pilot to production
2 sessions · 8 pointsSession 1The data question everyone skips at the start of learning systems
- Record what was learned when adaptive AI and continuous learning systems did not go as planned.
- Record the reasoning behind each architectural choice in learning systems.
- Decide what will be stopped to create capacity for adaptive AI and continuous learning systems.
- Estimate compute and licensing cost for learning systems at expected and at peak load.
Session 2Building lasting competence in learning systems
- Agree the indicators that will show whether adaptive AI and continuous learning systems is improving.
- Rehearse the briefing on learning systems that would follow an incident.
- Identify the data already collected that bears on adaptive AI and continuous learning systems.
- Classify the data in learning systems and apply access controls that match the classification.
Choose the package that suits you
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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