Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI Governance and Compliance addresses the fast-hardening expectation that AI systems be safe, fair and accountable. You learn the leading risk and governance frameworks, the emerging regulatory landscape — the EU AI Act, the NIST AI Risk Management Framework and sector rules — and how to translate them into practice. The course covers the pillars of responsible AI: fairness and bias, transparency and explainability, privacy, and human oversight, along with the documentation and audit trails that demonstrate compliance. You finish able to help an organisation deploy AI that stands up to scrutiny. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI governance and compliance — risk frameworks, emerging regulation such as the EU AI Act, fairness and transparency, and building accountable AI practices.
📋 Course Objectives
1. Apply AI risk and governance frameworks.
2. Interpret regulation such as the EU AI Act and NIST AI RMF.
3. Assess fairness, bias and transparency in AI systems.
4. Build documentation, audit and oversight practices.
5. Operationalise responsible-AI principles.
👥 Who Should Enroll?
• Risk, compliance and legal professionals
• AI product and governance leads
• Data scientists working in regulated settings
• Students of AI policy and ethics
🚀 Key Learning Outcomes
• The ability to guide compliant AI deployment.
• A governance and risk framework you can apply.
• Fluency in the emerging AI regulatory landscape.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and AI Governance and Compliance Foundations
Develop a comprehensive understanding of AI fundamentals, including machine learning, deep learning, and neural networks • Analyze the mathematical prerequisites for AI, including linear algebra, calculus, and probability theory • Design a framework for AI governance and compliance, incorporating regulatory requirements and industry standards
Data Engineering, Preprocessing, and Feature Pipelines
Implement data engineering pipelines using tools such as Apache Beam, Apache Spark, or AWS Glue • Evaluate data preprocessing techniques, including data cleaning, feature scaling, and data transformation • Configure feature pipelines using libraries such as scikit-learn, TensorFlow, or PyTorch
Model Architecture, Algorithm Design, and AI Governance and Compliance Methods
Design and implement model architectures using convolutional neural networks, recurrent neural networks, or transformers • Analyze algorithm design principles, including optimization techniques, regularization methods, and hyperparameter tuning • Develop AI governance and compliance methods, incorporating explainability, transparency, and accountability
Training, Hyperparameter Optimization, and Evaluation
Train machine learning models using stochastic gradient descent, Adam optimizer, or other optimization algorithms • Evaluate hyperparameter optimization techniques, including grid search, random search, or Bayesian optimization • Configure model evaluation metrics, including accuracy, precision, recall, F1 score, or mean squared error
Deployment, MLOps, and Production Workflows
Deploy machine learning models using containerization tools such as Docker, Kubernetes, or TensorFlow Serving • Implement MLOps workflows, incorporating continuous integration, continuous deployment, and continuous monitoring • Design production workflows, including data ingestion, model serving, and monitoring
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze ethical considerations in AI development, including fairness, transparency, and accountability • Evaluate bias mitigation techniques, including data preprocessing, feature engineering, or model regularization • Develop responsible AI practices, incorporating human-centered design, value alignment, and stakeholder engagement
Industry Integration, Business Applications, and Case Studies
Implement AI solutions in various industries, including healthcare, finance, or retail • Analyze business applications of AI, including customer service, marketing, or supply chain management • Evaluate case studies of successful AI implementations, including challenges, opportunities, and best practices
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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