Most organisations now hold more data about fine-tuning generative models than they can actually act on. Technology choices around the practice within digital and data-driven work are easy to reverse on paper and expensive to reverse in practice. Participants who influence the digital and data-driven work discipline without directly controlling it will find the content directly usable. Mature organisations treat fine-tuning generative models as a standing capability rather than a project that finishes. Participants leave with a method for this strand of digital and data-driven work, not a set of opinions about it. Ambition around the wider digital and data-driven work agenda outruns capacity unless the sequencing is deliberate. The teaching approach is deliberately practical: participants build a delivery roadmap for fine-tuning generative models as they go. The course leaves participants able to diagnose weaknesses in this aspect of digital and data-driven work before they become incidents. The final session converts the week's work on the wider digital and data-driven work agenda into commitments with owners and dates.
Expected Learning Outcomes
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Build the internal skills to operate fine-tuning generative models without permanent vendor dependency.
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Identify the failure points in fine-tuning generative models most likely to cause loss, and control them first.
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Establish version control and rollback for every component of fine-tuning generative models that reaches production.
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Prepare the human side of fine-tuning generative models: who is retrained, who is redeployed, and when they are told.
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Design a practical operating method for fine-tuning generative models that fits the organisation's size and maturity.
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Specify the data fine-tuning generative models depends on, where it originates and who is accountable for its quality.
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Assign clear ownership for each element of fine-tuning generative models across the functions involved.
Who Should Attend
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Vendor and contract managers overseeing suppliers involved in fine-tuning generative models.
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Team leaders and supervisors who put fine-tuning generative models into practice day to day.
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Business analysts translating requirements for fine-tuning generative models.
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Public sector officials applying fine-tuning generative models within a regulated framework.
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Risk and compliance staff assessing the controls around fine-tuning generative models.
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Chief information officers accountable for the investment in fine-tuning generative models.
Course Modules
Open any module to see its sessions and topics.
01
Fine-tuning generative models: vendor selection and avoiding lock-in
2 sessions · 8 points
Session 1Making fine-tuning generative models secure without making it unusable
Define the trigger that would require fine-tuning generative models to be redesigned.
Identify the skills the team lacks to operate fine-tuning generative models independently.
Measure the current quality of the data feeding fine-tuning generative models before assuming it is usable.
Decide which legacy process fine-tuning generative models retires, and set the date.
Session 2The decisions in fine-tuning generative models that cannot be delegated
Rank the weaknesses in fine-tuning generative models by consequence rather than by ease of fixing.
Test fine-tuning generative models against edge cases drawn from real historical records.
Set the metrics that will show whether fine-tuning generative models is drifting from its intended behaviour.
Confirm that those complying with fine-tuning generative models understand why it exists.
02
Fine-tuning generative models: governance, ethics and explainability
2 sessions · 8 points
Session 1The governance fine-tuning generative models needs and the governance it does not
Agree the indicators that will show whether fine-tuning generative models is improving.
Verify six months later that changes to fine-tuning generative models have held.
Confirm the retention and deletion rules applied to data inside fine-tuning generative models.
Record what was learned when fine-tuning generative models did not go as planned.
Session 2Choosing a supplier for fine-tuning generative models without being captured
Identify every system fine-tuning generative models must read from or write to.
Specify the fallback path when fine-tuning generative models is unavailable.
Distinguish symptoms from causes when fine-tuning generative models underperforms.
Assess the regulatory obligations fine-tuning generative models triggers in each jurisdiction.
03
Fine-tuning generative models: security, privacy and regulatory obligation
2 sessions · 8 points
Session 1The pilot that actually settles the argument about fine-tuning generative models
Estimate compute and licensing cost for fine-tuning generative models at expected and at peak load.
Record the reasoning behind each architectural choice in fine-tuning generative models.
Build the user briefing that explains what fine-tuning generative models does and does not decide.
Classify the data in fine-tuning generative models and apply access controls that match the classification.
Session 2Proving fine-tuning generative models paid for itself
Record the rationale for each significant choice made about fine-tuning generative models.
Arrange the handover of fine-tuning generative models so capability survives staff changes.
List the data sources fine-tuning generative models consumes and confirm each has a named owner.
Prepare the summary of fine-tuning generative models that senior management will read.
04
Fine-tuning generative models: measuring benefit and retiring what it replaces
2 sessions · 8 points
Session 1The paperwork for fine-tuning generative models that is actually needed
Map the handovers in fine-tuning generative models between functions and secure them.
Agree who is on call for fine-tuning generative models outside working hours.
Assign responsibility for keeping documentation of fine-tuning generative models current.
Reduce the variation in how fine-tuning generative models is carried out between teams.
Session 2Reviewing fine-tuning generative models when nothing has gone wrong
Prepare the response for the most likely failure in fine-tuning generative models.
Verify that fine-tuning generative models still performs when input volume doubles unexpectedly.
Check that records of fine-tuning generative models answer the questions likely to be asked.
Confirm that reporting on fine-tuning generative models reaches the people who can act.
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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