Every model in production is a source of risk as well as value. A credit scorecard can drift as the economy shifts, a fraud model can be gamed, a forecasting pipeline can fail silently when an upstream table changes, and a chatbot can leak confidential data. Over ten days you learn to manage these exposures as deliberately as credit or operational risk, starting with a machine learning risk taxonomy, FMEA and bowtie analysis of a model pipeline, and controls aligned with ISO 31000, ISO/IEC 23894 and the NIST AI Risk Management Framework.
Quantitative labs in Python cover stress and scenario testing, challenger models, calibration, conformal prediction, the population stability index and adversarial threats mapped with MITRE ATLAS and the OWASP Top 10 for LLM applications. Later days set risk appetite and key risk indicators, design human override and fallback procedures, and assess vendor models. Model risk managers, data scientists, operational risk officers, internal auditors and information security staff in banking, insurance, telecoms, health and government will find it directly relevant. Afterwards you can rate, mitigate and report the risk of any model. Delivery is classroom, online or in-house, with a CPD-accredited certificate.
As machine learning moves into lending, claims handling, fraud screening, network planning and patient triage, model failure has become a business risk in its own right. Errors are often silent: a model keeps producing confident scores long after the population it was trained on has changed, or after an upstream system begins sending malformed values. New threats have appeared alongside the familiar ones, including data poisoning, prompt injection, model theft and privacy attacks on training data, and many organisations still have no register, appetite statement or named owner for any of them.
The Risk Management for Data Science & Machine Learning in Practice Training Course applies the established cycle of identification, assessment, treatment, monitoring and reporting to every stage of the model lifecycle. Days one to five build the foundations: a risk taxonomy, structured identification methods, data risk, model error and its business impact, and risk appetite. Days six to ten go further into quantification and control, with uncertainty estimation, stress testing, adversarial robustness, security threat modelling, third-party and generative AI risk, resilience planning, incident response and board-level reporting.
Each concept is tried on real models. Participants work in Python notebooks on credit, fraud and demand forecasting data, break the models deliberately and measure what happens, then design the controls that would have caught the failure. The course closes with an integrated risk assessment of a complete machine learning system, presented to a simulated risk committee.
Participants completing the course will be able to:
Participants return to work with:
Learning is evidence-led: participants test risks on working models rather than discussing them in the abstract. Methods include:
Day 1: The Risk Landscape of Machine Learning
Day 2: Structured Risk Identification
Day 3: Data Risk
Day 4: Model Risk and the Cost of Error
Day 5: Risk Appetite, Materiality and Ownership
Day 6: Quantifying Uncertainty and Stress Testing
Day 7: Security and Adversarial Threats
Day 8: Generative AI and Third-Party Model Risk
Day 9: Monitoring, Incidents and Resilience
Day 10: Integrated Risk Assessment Project
The programme brings together the technical and risk specialists who share responsibility for models, such as:
Delegates who attend all ten days and complete the labs, risk workshops and final assessment receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
Alongside the certificate, participants keep the risk register, threat model and committee report they produced, a practical record of model risk work that can support annual appraisals and continuing professional development files.
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 Risk Management for Data Science & Machine Learning in Practice programme, you'll be able to classify the risks that data science and machine learning introduce, from data quality and model error to security, privacy, conduct and third-party exposures, apply fmea, bowtie analysis and pre-mortem workshops to identify failure modes across a model pipeline, quantify the business impact of model error using cost-weighted metrics, calibration checks and stress testing, and estimate prediction uncertainty with bootstrapping and conformal prediction, and set decision thresholds accordingly. The full breakdown of topics is covered session by session in the Course Outline tab above.
The programme brings together the technical and risk specialists who share responsibility for models, such as: Model risk managers and model validation analysts, Data scientists and machine learning engineers who own production models, Enterprise, operational and credit risk officers, Chief risk officers and heads of risk in banks, insurers and microfinance institutions, Internal auditors and IT auditors reviewing analytics and automated decisions, Information security and cyber risk specialists, Actuaries and quantitative analysts who rely on predictive models, and Risk and compliance staff in telecoms, health, energy and public agencies.
Risk Management for 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.
Risk Management for 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 Risk Management for 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 Risk Management for 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.
Risk Management for 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 Risk Management for 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 — Risk Management for 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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