Live Online Governance and Compliance in Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Oct 19, 2026 · from USD 1,700
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.
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
- Oct 19, 2026, Nov 16, 2026, Dec 14, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
By the end of the course, participants will 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.
- Explain model behaviour using SHAP, LIME and partial dependence techniques.
- Perform independent validation, including back-testing, sensitivity and stress tests.
- Implement reproducibility, versioning and lineage with tools such as Git, DVC and MLflow.
- Monitor deployed models for drift, performance decay and incidents.
- Prepare documentation and evidence packs for internal audit and regulators.
What you'll learn
Key Takeaways
Participants finish the course with:
- A governance policy outline and model lifecycle for their organisation.
- A model inventory and risk tiering template.
- A completed model card, data sheet and validation report from their own project.
- A fairness and explainability testing notebook in Python.
- A monitoring plan with drift thresholds and incident response steps.
- An audit evidence checklist covering data, model and deployment controls.
- A data protection impact assessment template adapted for machine learning projects.
- A change control and re-validation checklist for models moving through MLflow environments.
Training Methodology
The course combines governance workshops with technical labs, including:
- Python notebooks that apply a control to each stage of the model lifecycle
- Governance clinics where participants draft real policies and templates
- Peer validation exercises in which each person reviews another's model
- Simulated model risk committee sessions
- A ten-day project that builds a full governance pack for one model
Day 1: Why Governance Matters for Machine Learning
- Failures of automated decisions and their consequences
- Machine learning lifecycle from problem framing to retirement
- Principles of responsible and trustworthy AI
- Overview of the NIST AI Risk Management Framework and ISO/IEC 42001
- Three lines of defence applied to models
- Accountability for automated decisions: who signs off and who answers for outcomes
- Exercise: mapping governance gaps in a sample project
Day 2: Governance Operating Model
- Policies, standards and decision rights for analytics
- Roles: model owner, developer, validator and approver
- Model inventory and risk tiering
- Ethics review and use case approval
- Third-party and vendor model risk
- Model risk appetite statements and materiality criteria
- Workshop: drafting a model governance policy
Day 3: Data Governance, Privacy and Consent
- Data quality dimensions and profiling in Python
- Lineage, provenance and data catalogues
- Data protection principles, lawful basis and minimisation
- Anonymisation, pseudonymisation and privacy-enhancing techniques
- Retention, cross-border transfer and data sharing
- Data protection impact assessments for machine learning use cases
- Lab: data quality and privacy checks on a customer data set
Day 4: Responsible Model Development
- Problem definition, target design and feature review
- Leakage, sampling and dataset splitting errors
- Baselines, cross-validation and hyperparameter discipline
- Documentation during development: experiment logs and model cards
- Reproducible environments and version control
- Feature selection review for proxies of protected characteristics
- Lab: building a documented credit risk model
Day 5: Fairness and Bias
- Sources of bias in data and labels
- Fairness definitions: demographic parity, equal opportunity and trade-offs
- Measuring disparate impact in Python
- Mitigation by pre-processing, in-processing and post-processing
- Legal and ethical perspectives on protected attributes
- Fairness thresholds and documenting accepted trade-offs
- Lab: fairness audit and mitigation of a scoring model
Day 6: Explainability and Transparency
- Global and local explanation methods
- SHAP, LIME and partial dependence plots
- Interpretable models versus post-hoc explanation
- Communicating decisions to affected individuals
- Reason codes and human-in-the-loop review
- Counterfactual explanations and adverse action notices
- Lab: explanation report for a high-risk model
Day 7: Validation and Testing
- Independent validation scope and conceptual soundness review
- Back-testing, benchmarking and challenger models
- Sensitivity, stress and adversarial testing
- Performance thresholds and acceptance criteria
- Writing a validation report with findings and ratings
- Validating vendor and pre-trained models with limited transparency
- Lab: validating a peer's model
Day 8: MLOps, Deployment and Change Control
- CI/CD pipelines for models and approval gates
- Experiment tracking and model registry with MLflow
- Staging, canary releases and rollback
- Change management and re-validation triggers
- Access control, secrets and security for model endpoints
- Infrastructure as code and environment parity between development and production
- Lab: registering and promoting a model through environments
Day 9: Monitoring, Incidents and Audit
- Data drift, concept drift and performance monitoring
- Alerting thresholds and retraining policy
- Incident management and model decommissioning
- Internal audit approach to machine learning
- Regulatory inquiries and evidence preparation
- Model performance dashboards for risk committees and senior management
- Exercise: responding to a drift incident and audit request
Day 10: Integrated Governance Project
- Assembling the governance pack for a chosen model
- Model card, data sheet, validation summary and monitoring plan
- Approval memo and residual risk statement
- Presentations to a mock model risk committee
- Peer and facilitator feedback
- Mapping the governance pack to internal audit and regulatory evidence requests
- Action plan and course review
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
- Public sector data and policy teams using algorithmic tools
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.
Choose Where You Want to Learn
Governance and Compliance in 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 Governance and Compliance in Data Science & Machine Learning in Practice Training Course
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 November 16, 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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