Machine learning has moved from research teams into lending, insurance, hiring, health and public services, and regulators have followed. Data protection laws govern how training data is collected and used, sector supervisors expect model risk management, and new AI-specific rules classify systems by the harm they can cause. For practitioners, the difficulty is translating legal language into engineering decisions made every day.

The Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course bridges that gap over ten days. The opening block builds the regulatory map: privacy principles, data subject rights, automated decision-making, intellectual property and consent, followed by AI risk frameworks and management system standards. Participants learn to classify a use case by risk, decide what documentation and human oversight it requires, and involve legal, compliance and security colleagues at the right moment.

The second block turns requirements into code and process. Participants practise data minimisation and pseudonymisation, test models for bias, generate explanations, write model cards and datasheets, version data and experiments, and set up drift and performance monitoring with incident procedures. The closing project requires a complete governed machine learning solution, with an assessment file that a reviewer could actually audit.

By the end of the course, participants will be able to:

• Map the privacy, AI and sector rules that apply to a data science or machine learning use case.
• Classify AI systems by risk tier and determine the controls each requires.
• Establish lawful bases, consent and purpose limits for training and inference data.
• Apply data minimisation, pseudonymisation and other privacy-preserving techniques in Python.
• Test models for bias and measure fairness across protected groups.
• Produce explanations of model behaviour that suit users, auditors and regulators.
• Prepare model cards, datasheets, lineage records and validation reports.
• Monitor deployed models for drift and manage incidents and change.

The course is intended for those who build, approve or oversee data-driven systems, such as:

• Data scientists and machine learning engineers
• Analytics and artificial intelligence team leads
• Data protection officers and privacy counsel
• Compliance and regulatory affairs managers
• Model risk and internal audit professionals in banks and insurers
• Health informatics and public sector data specialists
• Product owners for AI-enabled services
• Policy advisers shaping digital and data regulation

Participants leave with:

• A regulatory map linking legal duties to specific stages of the machine learning lifecycle.
• A risk classification worksheet for new AI use cases.
• Python notebooks for privacy-preserving preparation and bias testing.
• A model card and validation report template for review boards.
• A monitoring and incident response outline for deployed models.
• A governed capstone model with a full assessment file.

The ten days alternate legal analysis with technical work so that each requirement is seen in code. Participants experience:

• Short regulatory briefings tied to concrete use cases
• Python labs in notebooks using pandas, scikit-learn and explainability libraries
• Case files of model failures and regulatory enforcement themes
• Mock review board sessions where project files are challenged
• A team capstone that is assessed against a governance checklist

Participants who attend the full ten days and complete the labs and capstone receive a CPD-accredited Certificate of Completion from Vision Reach Global Consultancy.