Assess data readiness and quality before setting delivery timelines for a machine learning project.
Project Management
Delivering Machine Learning Projects from Data Readiness to Model Deployment
Manage machine learning projects end to end, covering data readiness assessment, model development iterations, validation and production deployment governance.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Machine learning projects more often fail because of unclear data readiness, unmanaged model iteration and weak deployment governance than because of algorithm choice, yet many project managers apply standard software delivery methods to them unchanged. This course adapts project management practice to that reality. It covers assessing data readiness and quality before committing to a delivery timeline, planning iterative model development cycles that lack the deterministic milestones of conventional software work, coordinating validation and bias testing across cross-functional teams, and governing the transition from prototype to monitored production deployment. Teaching combines data readiness assessment exercises with realistic datasets and deployment governance case discussion. Participants leave with a project plan template that reflects the genuine uncertainty of machine learning delivery and a framework for communicating that uncertainty credibly to sponsors.
Expected Learning Outcomes
Plan iterative model development cycles that accommodate experimentation and uncertain outcomes.
Coordinate cross-functional teams spanning data engineering, data science and business stakeholders.
Oversee model validation, bias testing and performance benchmarking before deployment approval.
Govern the transition from prototype to production using defined deployment readiness criteria.
Manage ongoing model monitoring, drift detection and retraining as a continuing project activity.
Communicate machine learning project status and risk to stakeholders unfamiliar with model uncertainty.
Who Should Attend
Project managers assigned to data science or machine learning delivery teams.
Data science leads who need project management structure without losing experimentation flexibility.
Programme managers overseeing portfolios that include artificial intelligence initiatives.
Product owners responsible for features built on machine learning models.
IT delivery managers coordinating deployment of models into production systems.
Business sponsors funding machine learning projects who need realistic timeline expectations.
Course Modules
Select any module to see its sessions and points.
01Assessing Data Readiness Before Committing to Delivery
2 sessions · 8 points
Session 1Evaluating Data Quality, Volume and Access
- Assess data quality dimensions including completeness, accuracy, consistency and timeliness.
- Evaluate whether available data volume and history are sufficient for the intended model.
- Confirm data access, ownership and governance approvals before planning delivery timelines.
- Document data readiness findings in a structured assessment that informs the project plan.
Session 2Setting Realistic Expectations from Data Constraints
- Translate data readiness gaps into explicit project risks and mitigation options.
- Negotiate scope or timeline adjustments with sponsors when data readiness is insufficient.
- Plan data preparation and labelling work as a distinct, resourced phase of the project.
- Avoid common planning errors that assume model development can start before data is ready.
02Managing Iterative Model Development
2 sessions · 8 points
Session 1Planning Experimentation Cycles
- Structure model development into iterative experimentation cycles with defined evaluation checkpoints.
- Set success criteria for each cycle that distinguish promising results from dead ends.
- Track experiments systematically to avoid repeated work and support reproducibility.
- Communicate iteration progress to stakeholders without overstating certainty of eventual success.
Session 2Coordinating Cross-Functional Contributors
- Coordinate data engineers, data scientists, subject matter experts and business stakeholders across cycles.
- Resolve handoff friction between data engineering and data science within the same project.
- Involve business stakeholders in interpreting model outputs to keep development aligned to need.
- Manage capacity conflicts when data scientists split time across multiple concurrent projects.
03Validation, Bias and Deployment Readiness
2 sessions · 8 points
Session 1Validating Model Performance and Fairness
- Oversee model validation using held-out data and performance benchmarks agreed with stakeholders.
- Coordinate bias and fairness testing across relevant population segments before deployment.
- Document validation results and known limitations transparently for governance review.
- Decide when validation results justify proceeding, iterating further or halting the project.
Session 2Governing the Path to Production
- Define deployment readiness criteria covering performance, monitoring and rollback capability.
- Coordinate with engineering teams on integration, scaling and infrastructure requirements.
- Secure governance sign-off appropriate to the model's risk level and business impact.
- Plan a phased or shadow deployment approach to limit exposure to unexpected model behaviour.
04Monitoring, Retraining and Communication
2 sessions · 8 points
Session 1Monitoring Models as an Ongoing Project Activity
- Establish drift detection and performance monitoring as continuing project deliverables, not afterthoughts.
- Define triggers and ownership for retraining or retiring an underperforming model.
- Plan resourcing for ongoing monitoring beyond the initial project delivery timeline.
- Track monitoring findings as inputs to future project planning and prioritisation.
Session 2Communicating Machine Learning Status to Stakeholders
- Translate technical model metrics into business-relevant status language for stakeholders.
- Set expectations about uncertainty and iteration inherent to machine learning delivery.
- Report risk transparently when validation results fall short of stakeholder expectations.
- Capture lessons learned to improve estimation and planning for future machine learning projects.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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