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
Edge AI for Healthcare: TinyML for Medical Wearables teaches how to bring machine learning onto the smallest devices — the wearables and sensors that monitor health in real time. You learn the constraints that define edge AI in medicine: tiny memory and power budgets, the need for privacy that keeps data on-device, and reliability where results matter. The course covers building and compressing models with techniques like quantisation and pruning, deploying them with TinyML frameworks, and applying them to real signals such as heart rate, activity and arrhythmia detection. You finish able to design an on-device medical-AI solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers TinyML and edge AI for healthcare — deploying efficient machine-learning models on wearables and medical devices for on-device health monitoring.
📋 Course Objectives
1. Explain the constraints of edge AI on medical devices.
2. Build efficient models for wearables.
3. Compress models with quantisation and pruning.
4. Deploy with TinyML frameworks.
5. Apply on-device inference to health signals.
👥 Who Should Enroll?
• Embedded and biomedical engineers
• Health-tech and wearables developers
• Data scientists working with health signals
• Students of edge AI
🚀 Key Learning Outcomes
• The ability to design on-device medical AI.
• A TinyML health-monitoring project.
• Skills bridging embedded systems and machine learning.
• 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 Edge AI Foundations
Apply linear algebra and calculus concepts to optimize AI model performance in healthcare applications • Design and implement neural network architectures using TensorFlow and PyTorch for medical image analysis • Evaluate the trade-offs between model complexity and computational resources in edge AI deployments
Data Engineering, Preprocessing, and Feature Pipelines
Develop and deploy data pipelines using Apache Beam and Google Cloud Dataflow for large-scale medical data processing • Configure and optimize data preprocessing techniques such as normalization and feature scaling for improved model accuracy • Analyze and visualize medical dataset distributions using Matplotlib and Seaborn to identify potential biases
Model Architecture, Algorithm Design, and Edge AI Methods
Implement and evaluate various deep learning architectures such as CNNs and RNNs for medical signal processing and analysis • Design and optimize model architectures for edge AI deployments using techniques such as pruning and quantization • Develop and test algorithms for real-time data processing and anomaly detection in medical wearables
Training, Hyperparameter Optimization, and Evaluation
Configure and execute hyperparameter tuning using GridSearchCV and RandomSearchCV for optimal model performance • Evaluate and compare the performance of different machine learning models using metrics such as accuracy and F1-score • Develop and implement strategies for addressing overfitting and underfitting in medical AI models
Deployment, MLOps, and Production Workflows
Deploy and manage AI models in production environments using Docker and Kubernetes • Develop and implement MLOps pipelines for continuous model monitoring and updating • Configure and optimize model serving infrastructure for low-latency and high-throughput inference
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and address potential biases in medical AI datasets and models using techniques such as data augmentation • Develop and implement strategies for ensuring transparency and explainability in AI decision-making • Evaluate and mitigate the risks of AI model drift and concept drift in medical applications
Industry Integration, Business Applications, and Case Studies
Develop and pitch business cases for AI-powered medical wearables and devices • Analyze and evaluate the market potential and competitive landscape of AI in healthcare • Design and implement AI-powered solutions for real-world medical challenges and use cases
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Beam |
| Covered Tool / Platform | Google Cloud Dataflow |
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