Monitoring and Evaluation of Data Science & Machine Learning in Practice Training 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.

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.

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 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.

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

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.