Live Online Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Nov 9, 2026 · from USD 1,700
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.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Nov 9, 2026, Dec 7, 2026, Jan 4, 2027
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
Participants completing the ten days will 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.
- Build a production monitoring plan covering data drift, prediction drift, performance decay, latency and fairness.
- Select and apply experimental and quasi-experimental designs to estimate the causal impact of model-driven decisions.
- Assess the value for money of an ML initiative through cost-effectiveness and cost-benefit analysis.
- Apply the OECD-DAC evaluation criteria to judge the relevance, effectiveness, efficiency, impact and sustainability of an AI deployment.
- Communicate evaluation findings and recommendations to technical teams, managers and oversight bodies.
What you'll learn
Key Takeaways
Participants complete the programme with:
- A theory of change and logframe for a machine learning initiative of their choice.
- An indicator reference sheet with definitions, formulas, baselines and reporting frequency.
- A Python notebook that produces an offline evaluation report with subgroup and calibration analysis.
- A drift and fairness monitoring dashboard configured on a live case model.
- An impact evaluation design, including sample size and power calculations, for a model rollout.
- A cost-effectiveness worksheet comparing a model-led process with the existing approach.
- A complete evaluation report and steering committee briefing for a deployed model.
Training Methodology
Each day pairs evaluation concepts with analysis of realistic data, through:
- Guided Python labs in Jupyter using pandas, scikit-learn, statsmodels and Evidently
- Case files from microfinance, public health, social protection and e-commerce, each with logs and outcome data
- Design clinics where teams draft and critique results frameworks and indicator matrices
- Simulated rollouts that let participants run and analyse their own experiments
- Structured debates on attribution, ethics and the quality of evidence
- A capstone evaluation report presented to a mock steering committee
Day 1: Why Machine Learning Needs Monitoring and Evaluation
- Where models fail after launch: drift, misuse, feedback loops and unmeasured benefits
- Monitoring versus evaluation for data science initiatives
- Stakeholders, decision owners and evaluation questions
- Evaluation points across the model lifecycle from design to retirement
- Evaluation standards and ethical principles for data-driven systems
- Case review: a targeting model that looked accurate but missed its goal
- Exercise: drafting evaluation questions for a case model
Day 2: Theories of Change and Results Frameworks for ML
- Mapping how a prediction becomes a decision, an action and an outcome
- Assumptions and risks along the causal chain
- Logframes and results chains adapted to machine learning systems
- Contribution versus attribution for model-supported decisions
- Defining success with business owners and affected communities
- Exercise: a theory of change for a credit scoring rollout
- Peer critique of results frameworks
Day 3: Indicator Design Across the Model Stack
- Data quality indicators: completeness, timeliness, validity and representativeness
- Model indicators: accuracy, precision, recall, AUC, RMSE and calibration
- Decision indicators: adoption rates, override rates and turnaround times
- Outcome indicators linked to programme or business results
- SMART criteria, baselines and target setting
- Indicator reference sheets and data source plans
- Exercise: building an indicator matrix for a health risk prediction model
Day 4: Offline Model Evaluation in Python
- Holdout, k-fold and time-based validation with scikit-learn
- Choosing metrics that reflect the cost of each type of error
- Calibration curves and the Brier score
- Confusion matrices and threshold analysis
- Error analysis by region, gender, age and other subgroups
- Leakage checks and sanity tests on evaluation results
- Lab: producing a reproducible evaluation report
Day 5: Monitoring Plans and Data Collection
- Designing a monitoring plan: what, how often, by whom and with what triggers
- Logging predictions, inputs, outcomes and user actions
- Delayed ground truth and proxy indicators
- Feedback collection from users and affected people
- Data protection and consent in monitoring data
- Model health dashboards in Power BI or Python
- Lab: first-week synthesis and a monitoring plan for the case model
Day 6: Production Monitoring of Drift, Performance and Fairness
- Population stability index, Kolmogorov-Smirnov and Jensen-Shannon drift measures
- Concept drift and delayed performance measurement
- Tracking fairness metrics over time, from demographic parity to equal opportunity
- Alert thresholds and escalation routes
- Open-source monitoring with Evidently and whylogs
- Diagnosing the cause when a model degrades
- Lab: configuring drift and fairness monitoring on incoming case data
Day 7: Experimental Impact Evaluation of Model-Driven Decisions
- The counterfactual problem for machine learning deployments
- A/B tests and randomised rollouts of model-led processes
- Sample size, statistical power and minimum detectable effects
- Stratification, cluster randomisation and spillovers
- Analysing experiments with statsmodels
- Ethical considerations when withholding a model's recommendations
- Lab: designing and analysing a randomised rollout
Day 8: Quasi-Experimental and Mixed-Method Approaches
- Difference-in-differences with staggered rollouts
- Regression discontinuity around score thresholds
- Propensity score matching and weighting
- Interrupted time series for system-wide deployments
- Qualitative methods: user interviews, process tracing and outcome harvesting
- Triangulating quantitative and qualitative evidence
- Lab: estimating impact where randomisation was not possible
Day 9: Value for Money, OECD-DAC Criteria and Adaptive Learning
- Cost-effectiveness and cost-benefit analysis for ML initiatives
- Applying relevance, coherence, effectiveness, efficiency, impact and sustainability to AI systems
- Judging sustainability: maintenance burden, skills and data pipelines
- Learning loops from monitoring findings to retraining and redesign
- After-action reviews and adaptive management
- Reporting to boards, donors and regulators
- Lab: value-for-money assessment of the case model
Day 10: Integrated Evaluation Project
- Teams receive documentation, logs and outcome data for a deployed model
- Framing evaluation questions and selecting methods
- Running the monitoring, offline and impact analyses
- Rating performance against the OECD-DAC criteria
- Writing findings, conclusions and recommendations
- Presenting to a mock steering committee
- Personal action plan for evaluating models in the participant's organisation
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
- Digital transformation and innovation officers in government ministries
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.
Choose Where You Want to Learn
Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Monitoring and Evaluation of Data Science & Machine Learning in Practice Training Course
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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