Select indicators that show whether continuous deployment for AI models is improving, and reject those that only look useful.
Building Continuous Deployment Pipelines for Artificial Intelligence Models
Learn to design, measure and defend your organisation's approach to continuous deployment for AI models.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Most organisations now hold more data about continuous deployment for AI models than they can actually act on. Boards are asking for measurable returns from AI models, not demonstrations. It is designed for mixed groups, so that the wider digital and data-driven work agenda is examined from more than one functional angle. The result is the confidence to make and defend decisions about continuous deployment for AI models under scrutiny. Progress on the digital and data-driven work capability is usually lost in the gap between approval and execution. Comparative studies of AI models across sectors find the same handful of failure points recurring. Sessions alternate between guided analysis of continuous deployment for AI models and supervised application. It treats AI models as an operating discipline and equips participants to run it as one. Work concludes with a self-assessment of the wider digital and data-driven work agenda that participants can repeat annually.
Expected Learning Outcomes
Agree the retirement plan for the legacy process AI models replaces.
Plan the handover of continuous deployment for AI models so that capability is not lost when key staff move on.
Plan the migration path for AI models without an extended outage or a parallel-running trap.
Design a training and briefing approach that sustains competence in continuous deployment for AI models.
Evaluate the bias, fairness and explainability obligations attaching to AI models.
Design the pilot for continuous deployment for AI models so its result is decisive rather than merely encouraging.
Who Should Attend
Business analysts translating requirements for continuous deployment for AI models.
Solution architects designing how AI models fits the existing estate.
Programme managers coordinating delivery of continuous deployment for AI models across teams.
Risk and compliance staff assessing the controls around AI models.
Public sector officials applying continuous deployment for AI models within a regulated framework.
Specialists advising senior management on AI models.
Course Modules
Continuous deployment for AI models: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1The data question everyone skips at the start of continuous deployment for AI models
- Decide which legacy process continuous deployment for AI models retires, and set the date.
- Establish who is informed, consulted and accountable in AI models.
- Agree who is on call for continuous deployment for AI models outside working hours.
- Assess the regulatory obligations AI models triggers in each jurisdiction.
Session 2Building the method for AI models step by step
- Set out how exceptions to continuous deployment for AI models are requested and approved.
- Identify where judgement in AI models is legitimate and where it is not.
- Establish the boundary of continuous deployment for AI models and record what sits outside it.
- Confirm the retention and deletion rules applied to data inside AI models.
AI models: business case, scope and the data it depends on
2 sessions · 8 pointsSession 1Sizing AI models honestly before committing budget
- Estimate compute and licensing cost for continuous deployment for AI models at expected and at peak load.
- Test the procedure for AI models against a realistic scenario.
- Design the pilot for continuous deployment for AI models so that a negative result is still useful.
- Agree the indicators that will show whether AI models is improving.
Session 2Proving AI models paid for itself
- Prepare the summary of continuous deployment for AI models that senior management will read.
- Verify that AI models still performs when input volume doubles unexpectedly.
- Confirm that contractual obligations around continuous deployment for AI models are understood.
- Set the metrics that will show whether AI models is drifting from its intended behaviour.
AI models: governance, ethics and explainability
2 sessions · 8 pointsSession 1Closing out AI models and capturing what was learned
- Identify every system continuous deployment for AI models must read from or write to.
- Build the user briefing that explains what AI models does and does not decide.
- List the data sources continuous deployment for AI models consumes and confirm each has a named owner.
- Distinguish symptoms from causes when AI models underperforms.
Session 2Making continuous deployment for AI models work when resources are constrained
- Set out the decisions in continuous deployment for AI models that require sign-off and by whom.
- Measure the current quality of the data feeding AI models before assuming it is usable.
- Identify the skills the team lacks to operate continuous deployment for AI models independently.
- Plan how AI models is versioned and how a bad release is rolled back.
AI models: vendor selection and avoiding lock-in
2 sessions · 8 pointsSession 1The pilot that actually settles the argument about AI models
- Check that records of continuous deployment for AI models answer the questions likely to be asked.
- Confirm that reporting on AI models reaches the people who can act.
- Draft the minimum viable delivery roadmap for continuous deployment for AI models.
- Remove steps in AI models that add effort without adding assurance.
Session 2The governance AI models needs and the governance it does not
- Map the handovers in continuous deployment for AI models between functions and secure them.
- Test AI models against edge cases drawn from real historical records.
- Prepare the response for the most likely failure in continuous deployment for AI models.
- Define the service level AI models must meet and what happens when it is missed.
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
Complete your registration
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