A model can score well on a test set and still fail the people it was built to serve. Accuracy drifts as data changes, pilot gains vanish at scale, and few organisations can say whether a churn model, fraud score or targeting algorithm changed an outcome. The ten days teach you to monitor and evaluate machine learning with the rigour applied to any major investment or programme. Sessions cover theories of change and results frameworks for ML initiatives, indicator design across data, model, decision and outcome levels, holdout and cross-validation evidence, calibration, and error analysis by subgroup.
Later days turn to production monitoring of drift, latency and fairness with Python and Evidently, then impact evaluation through A/B tests, randomised rollouts, difference-in-differences and propensity score matching. You also apply the OECD-DAC criteria to an AI deployment and build learning loops that feed findings into retraining and redesign. It is intended for M&E officers, data scientists, model validators, analytics managers, internal auditors and programme managers in banks, insurers, health systems, NGOs and public agencies. Afterwards you can design an M&E plan for a model and judge its real-world contribution. Study in class, online or in-house for a CPD-accredited certificate.
Machine learning is now used to approve loans, prioritise patients, target cash transfers and forecast demand, yet the question of whether these systems work is often answered only once, on a test dataset, before launch. After deployment, data shifts, users override or ignore predictions, and benefits promised in the business case go unmeasured. Without a deliberate monitoring and evaluation system, organisations cannot tell a model that is quietly failing from one that is delivering value.
The Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course brings evaluation discipline to every stage of the model lifecycle. The first week builds the framework: a theory of change for an ML initiative, an indicator hierarchy linking data quality, model performance, decisions and outcomes, sound offline evaluation, and a monitoring plan. The second week deepens the practice with production monitoring of drift and fairness, experimental and quasi-experimental impact designs, cost-effectiveness analysis, evaluation against the OECD-DAC criteria and adaptive learning, ending with a full evaluation of a deployed model.
Participants learn by doing. Labs in Python use pandas, scikit-learn, Evidently and statsmodels on cases from microfinance credit scoring, health facility risk prediction and humanitarian targeting, and each team writes an evaluation report that a steering committee could act on.
Participants completing the ten days will be able to:
Participants complete the programme with:
Each day pairs evaluation concepts with analysis of realistic data, through:
Day 1: Why Machine Learning Needs Monitoring and Evaluation
Day 2: Theories of Change and Results Frameworks for ML
Day 3: Indicator Design Across the Model Stack
Day 4: Offline Model Evaluation in Python
Day 5: Monitoring Plans and Data Collection
Day 6: Production Monitoring of Drift, Performance and Fairness
Day 7: Experimental Impact Evaluation of Model-Driven Decisions
Day 8: Quasi-Experimental and Mixed-Method Approaches
Day 9: Value for Money, OECD-DAC Criteria and Adaptive Learning
Day 10: Integrated Evaluation Project
The course is relevant to anyone who must show whether models and analytics deliver, including:
Participants who attend every session, complete the daily labs and submit the capstone evaluation report earn a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
The certificate states the programme title, dates and mode of delivery. Your results framework, monitoring plan and evaluation report remain yours to adapt, and they offer clear evidence of applied learning for professional development records.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Monitoring and Evaluation of Data Science & Machine Learning in Practice programme, you'll be able to develop a theory of change and results framework that links a machine learning system to the decisions and outcomes it is meant to improve, design indicators at data, model, decision and outcome levels, with baselines, targets and data sources, evaluate models offline using suitable metrics, validation schemes, calibration checks and subgroup error analysis, and build a production monitoring plan covering data drift, prediction drift, performance decay, latency and fairness. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course is relevant to anyone who must show whether models and analytics deliver, including: Monitoring, evaluation and learning officers in programmes that use predictive tools, Data scientists and machine learning engineers responsible for models in production, Model validation and model risk staff in banks, insurers and microfinance institutions, Analytics and business intelligence managers reporting on model performance, Internal auditors and quality assurance staff reviewing automated decision systems, Programme managers in health, social protection and humanitarian agencies using targeting or risk scores, Impact evaluation specialists and researchers in development organisations, and Digital transformation and innovation officers in government ministries.
Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Monitoring and Evaluation of Data Science & Machine Learning in Practice starts November 9, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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