Models now approve loans, flag fraud, prioritise patients and rank job applicants, and a poorly governed model can cause financial loss, discrimination and regulatory action before anyone notices. Over ten days, participants learn to build and oversee machine learning that can withstand scrutiny. The first half sets out governance structures, data lineage, privacy and data protection, consent, documentation, model inventories, risk tiering and independent validation. The second half moves to technical practice in Python, including bias and fairness testing, explainability with SHAP and LIME, drift monitoring, reproducibility with MLflow and version control, and controlled deployment.
Labs use tabular credit, health and customer data, and a final project requires a complete governance pack for one model: model card, data sheet, validation report, monitoring plan and approval memo. Attention is given to emerging AI regulation, the NIST AI Risk Management Framework, ISO/IEC 42001 and sector expectations in general terms. The programme suits data scientists, machine learning engineers, model risk and validation officers, compliance and internal audit staff, data protection officers and analytics heads. It runs in the classroom, online or in-house with a CPD-accredited certificate from Vision Reach Global Consultancy.
Machine learning has moved from experiment to infrastructure, and with that shift comes accountability. Regulators, auditors and customers now expect organisations to explain how automated decisions are made, show that data was lawfully obtained and demonstrate that models are tested, monitored and reversible. Teams that cannot do so risk fines, loss of trust and costly rework.
The Governance and Compliance in Data Science & Machine Learning in Practice Training Course joins the technical and control perspectives that are too often kept apart. Early sessions establish the governance operating model, policies, roles and the model lifecycle, together with the data protection principles that apply to training data. Participants then build models in Python and apply controls as they go, covering data quality checks, leakage prevention, validation design, fairness metrics, interpretability and stress testing.
The later days address production. Delegates learn how to document, approve, deploy and monitor models, manage drift and incidents, and prepare for audits. Each participant develops a full governance pack on a model of their choice, receiving feedback from the facilitator and peers, so that the course ends with working artefacts rather than notes.
By the end of the course, participants will be able to:
Participants finish the course with:
The course combines governance workshops with technical labs, including:
Day 1: Why Governance Matters for Machine Learning
Day 2: Governance Operating Model
Day 3: Data Governance, Privacy and Consent
Day 4: Responsible Model Development
Day 5: Fairness and Bias
Day 6: Explainability and Transparency
Day 7: Validation and Testing
Day 8: MLOps, Deployment and Change Control
Day 9: Monitoring, Incidents and Audit
Day 10: Integrated Governance Project
The programme is designed for people who build, approve or assure analytical models, including:
Participants who attend all ten days and complete the labs and the governance project receive a CPD-accredited Certificate of Completion from Vision Reach Global Consultancy.
Alongside the certificate, each participant keeps the governance pack built during the course, including the model card, validation report and monitoring plan, as practical evidence of the controls they can apply to models in their own organisation.
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 Governance and Compliance in Data Science & Machine Learning in Practice programme, you'll be able to design a governance framework for data science and machine learning, with policies, roles, escalation routes and a risk-tiered model inventory, apply data protection and privacy principles to the collection and use of training data, test models for bias and fairness using appropriate metrics and mitigation methods, and explain model behaviour using shap, lime and partial dependence techniques. The full breakdown of topics is covered session by session in the Course Outline tab above.
The programme is designed for people who build, approve or assure analytical models, including: Data scientists and machine learning engineers, Model risk managers and independent validators, Compliance, legal and data protection officers, Internal and external auditors of analytics and technology, Heads of analytics, data and digital transformation, Product owners deploying automated decision systems, Risk officers in banks, insurers, telecoms and health organisations, and Public sector data and policy teams using algorithmic tools.
Governance and Compliance in 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.
Governance and Compliance in 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 Governance and Compliance in Data Science & Machine Learning in Practice starts October 19, 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 Governance and Compliance in 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.
Governance and Compliance in Data Science & Machine Learning in Practice Training Course is pitched at advanced 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 Governance and Compliance in 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 — Governance and Compliance in 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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