Define success criteria for robust machine learning data pipelines in business terms before any technology is selected.
Building Robust Data Pipelines for Machine Learning
A concise, decision-focused programme covering robust machine learning data pipelines end to end.
📍 Tunis🗓️ 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 robust machine learning data pipelines than they can actually act on. Technology choices around data pipelines are easy to reverse on paper and expensive to reverse in practice. The programme works equally well for those formalising this area of digital and data-driven work for the first time and those improving an existing approach. The professional literature on robust machine learning data pipelines converges on a small set of controls that reliably work. Participants gain a realistic view of what the digital and data-driven work discipline costs and what it returns. Every module pairs a short input on data pipelines with structured practice on the participant's own material. What blocks progress on robust machine learning data pipelines is usually unclear ownership rather than unclear intent. The course sets out a working method for data pipelines that participants can apply the week they return. Participants leave with a first-ninety-days plan for this area of digital and data-driven work rather than a set of notes.
Expected Learning Outcomes
Establish version control and rollback for every component of data pipelines that reaches production.
Align robust machine learning data pipelines with the wider objectives of the transformation programme rather than optimising it in isolation.
Compare the organisation's handling of data pipelines with recognised practice, and close the material gaps.
Design the pilot for robust machine learning data pipelines so its result is decisive rather than merely encouraging.
Establish monitoring that detects model or service degradation in data pipelines before users report it.
Integrate robust machine learning data pipelines into existing management routines rather than running it separately.
Who Should Attend
Public sector digital leads applying robust machine learning data pipelines under procurement and privacy rules.
Staff seconded into improvement work on data pipelines.
Data and analytics leads responsible for the pipelines behind robust machine learning data pipelines.
Product owners prioritising the roadmap for data pipelines.
Team leaders and supervisors who put robust machine learning data pipelines into practice day to day.
Operations managers whose processes are changed by data pipelines.
Course Modules
Robust machine learning data pipelines: measuring benefit and retiring what it replaces
2 sessions · 8 pointsSession 1What breaks first when robust machine learning data pipelines meets real volume
- Identify the skills the team lacks to operate robust machine learning data pipelines independently.
- Collect evidence on the present handling of data pipelines before proposing changes.
- Measure the current quality of the data feeding robust machine learning data pipelines before assuming it is usable.
- List the data sources data pipelines consumes and confirm each has a named owner.
Session 2Reviewing data pipelines when nothing has gone wrong
- Identify every system robust machine learning data pipelines must read from or write to.
- Estimate compute and licensing cost for data pipelines at expected and at peak load.
- Build the user briefing that explains what robust machine learning data pipelines does and does not decide.
- Test the procedure for data pipelines against a realistic scenario.
Data pipelines: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1Explaining data pipelines to people whose jobs it changes
- Review whether robust machine learning data pipelines is aligned with the objectives of the transformation programme.
- Specify the fallback path when data pipelines is unavailable.
- Establish who is informed, consulted and accountable in robust machine learning data pipelines.
- Define the service level data pipelines must meet and what happens when it is missed.
Session 2Building the method for data pipelines step by step
- Confirm that reporting on robust machine learning data pipelines reaches the people who can act.
- Decide which legacy process data pipelines retires, and set the date.
- Set the metrics that will show whether robust machine learning data pipelines is drifting from its intended behaviour.
- Check that records of data pipelines answer the questions likely to be asked.
Data pipelines: cost, licensing and total running expense
2 sessions · 8 pointsSession 1Where data pipelines typically breaks, and why
- Agree the indicators that will show whether robust machine learning data pipelines is improving.
- Reduce the variation in how data pipelines is carried out between teams.
- Rank the weaknesses in robust machine learning data pipelines by consequence rather than by ease of fixing.
- Name a single owner for each element of data pipelines.
Session 2Sizing robust machine learning data pipelines honestly before committing budget
- Assess the regulatory obligations robust machine learning data pipelines triggers in each jurisdiction.
- Write down the assumptions underpinning the approach to data pipelines.
- Prepare the response for the most likely failure in robust machine learning data pipelines.
- Confirm the retention and deletion rules applied to data inside data pipelines.
Data pipelines: from pilot to production
2 sessions · 8 pointsSession 1The governance data pipelines needs and the governance it does not
- Record the reasoning behind each architectural choice in robust machine learning data pipelines.
- Define the trigger that would require data pipelines to be redesigned.
- Plan how robust machine learning data pipelines is versioned and how a bad release is rolled back.
- Plan the sequence in which improvements to data pipelines will be introduced.
Session 2The pilot that actually settles the argument about data pipelines
- Compare the cost of robust machine learning data pipelines with the cost of its absence.
- Rehearse the briefing on data pipelines that would follow an incident.
- Test robust machine learning data pipelines against edge cases drawn from real historical records.
- Verify that data pipelines still performs when input volume doubles unexpectedly.
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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