Integrate production machine learning deployment into existing management routines rather than running it separately.
Designing Strategies to Deploy Machine Learning Models in Production
Move production machine learning deployment from general awareness to a repeatable, reviewable practice.
📍 Cairo🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
The documentation for production machine learning deployment is accurate only until the next release. Security and delivery speed are traded against each other in learning deployment whether or not anyone says so. The programme takes participants through this aspect of technology and systems delivery end to end, from framing the problem to closing it out. The version of production machine learning deployment described in the manual and the version practised on the floor tend to diverge over time. Every module pairs a short input on the practice within technology and systems delivery with structured practice on the participant's own material. They leave able to brief senior management on learning deployment in terms that support a decision. Across sectors, teams that rehearse production machine learning deployment outperform teams that only plan it. It is designed for mixed groups, so that learning deployment is examined from more than one functional angle. The final session converts the week's work on this area of technology and systems delivery into commitments with owners and dates.
Expected Learning Outcomes
Assign clear ownership for each element of learning deployment across the functions involved.
Define the availability, performance and recovery targets for production machine learning deployment.
Measure delivery and reliability of learning deployment with indicators teams trust.
Translate policy on production machine learning deployment into procedures that hold up under day-to-day pressure.
Control technical debt in learning deployment deliberately rather than by neglect.
Build incident response for production machine learning deployment with defined severity and escalation.
Who Should Attend
Information security specialists protecting production machine learning deployment.
IT governance and audit staff reviewing learning deployment.
Service desk and support leads handling incidents in production machine learning deployment.
Managers in small and medium organisations who own learning deployment alongside other duties.
Network and communications engineers supporting production machine learning deployment.
Operations staff who encounter the consequences of learning deployment directly.
Course Modules
Production machine learning deployment: incident response and severity
2 sessions · 8 pointsSession 1Monitoring production machine learning deployment from the user's point of view
- Review access rights on production machine learning deployment and remove what is no longer needed.
- Rehearse the briefing on learning deployment that would follow an incident.
- Check that production machine learning deployment still works when volumes rise unexpectedly.
- Agree the smallest change to learning deployment that would be visibly useful.
Session 2Building the method for learning deployment step by step
- Document the runbook for production machine learning deployment to the level a new engineer could use.
- Configure alerting on learning deployment that reflects what users experience.
- Write down the assumptions underpinning the approach to production machine learning deployment.
- Set the review interval for learning deployment and who attends.
Learning deployment: backup, recovery and continuity
2 sessions · 8 pointsSession 1Moving learning deployment from approval to execution
- Establish who is informed, consulted and accountable in production machine learning deployment.
- Run a load test on learning deployment at expected peak plus a margin.
- Review logging on production machine learning deployment for coverage and retention.
- Assess the exit route from any cloud or vendor dependency in learning deployment.
Session 2Getting other functions to support learning deployment
- Benchmark the organisation's production machine learning deployment against comparable operations.
- Define incident severity levels for learning deployment and the response each triggers.
- Restore a backup of production machine learning deployment in a test environment and time it.
- Collect evidence on the present handling of learning deployment before proposing changes.
Learning deployment: documentation, support and handover
2 sessions · 8 pointsSession 1Retiring the legacy part of learning deployment
- Inventory third-party dependencies inside production machine learning deployment and their update status.
- Agree what will be standardised in learning deployment and what will not.
- Identify the single points of failure in production machine learning deployment.
- Apply change control to learning deployment including emergency changes.
Session 2Designing production machine learning deployment around how it will fail
- Identify where judgement in production machine learning deployment is legitimate and where it is not.
- Distinguish symptoms from causes when learning deployment underperforms.
- Define acceptance criteria for production machine learning deployment in advance.
- Test the failover for learning deployment rather than assuming it works.
Learning deployment: build, pipeline and release discipline
2 sessions · 8 pointsSession 1Running an incident on learning deployment calmly
- Measure current load on production machine learning deployment and project it forward twelve months.
- Close out actions on learning deployment rather than leaving them open indefinitely.
- Confirm the support model and escalation path for production machine learning deployment.
- Define the trigger that would require learning deployment to be redesigned.
Session 2Documenting learning deployment so someone else can operate it
- Confirm every release of production machine learning deployment can be rolled back within a defined time.
- Record what was learned when learning deployment did not go as planned.
- Set delivery and reliability indicators for production machine learning deployment the team trusts.
- Set out the decisions in learning deployment that require sign-off and by whom.
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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