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
LCA for Smart Products and IoT Devices applies rigorous life-cycle thinking to a fast-growing and often-overlooked source of environmental impact: connected electronics. You learn LCA methodology — goal and scope, inventory and impact assessment under ISO 14040/44 — and apply it to the specific footprint of smart devices: rare materials and manufacturing, energy in use, connectivity, and the e-waste challenge at end of life. The course connects assessment to eco-design decisions that reduce impact. You finish able to scope and reason about an LCA for a connected product. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies life-cycle assessment to smart products and IoT devices — quantifying environmental impact from materials and manufacturing through use to end of life.
📋 Course Objectives
1. Apply ISO 14040/44 LCA methodology.
2. Build a life-cycle inventory for a smart device.
3. Assess materials, manufacturing and use-phase impacts.
4. Address connectivity and e-waste at end of life.
5. Turn results into eco-design decisions.
👥 Who Should Enroll?
• Sustainability and LCA professionals
• Product designers and electronics engineers
• IoT and hardware teams
• Students of industrial ecology
🚀 Key Learning Outcomes
• The ability to scope an LCA for a smart product.
• A device life-cycle assessment project.
• An eco-design-oriented approach.
• 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 LCA Foundations for Smart Products and IoT
Derive gradient descent update rules for multivariate cost functions using partial differential calculus and matrix operations • Construct probabilistic Bayesian networks to model uncertainty in IoT sensor data streams and device failures • Formulate lifecycle assessment (LCA) system boundaries and functional units for embedded electronics and connected device ecosystems
Data Engineering, Preprocessing, and Feature Pipelines for IoT
Architect Apache Kafka and MQTT broker topologies to ingest high-velocity telemetry from heterogeneous IoT device fleets • Implement sliding-window and tumbling-window aggregations on time-series sensor data using Pandas and Apache Flink • Engineer spectral and wavelet features from raw accelerometer and gyroscope signals for downstream anomaly detection models
Model Architecture, Algorithm Design, and LCA Methods
Design quantized neural network architectures (INT8, FP16) that satisfy latency constraints on ARM Cortex-M and ESP32 microcontrollers • Develop surrogate LCA models using gradient-boosted trees and Gaussian processes to approximate computationally expensive process simulations • Integrate physics-informed neural network layers that enforce conservation laws and material balance constraints during training
Training, Hyperparameter Optimization, and Evaluation
Execute distributed hyperparameter sweeps using Ray Tune and Optuna across Kubernetes clusters with early-stopping protocols • Calibrate probabilistic classification models with temperature scaling and Platt scaling to achieve reliable IoT device failure predictions • Compute normalized confusion matrices, Matthews correlation coefficients, and energy-adjusted F1 scores for imbalanced smart product datasets
Deployment, MLOps, and Production Workflows
Containerize inference pipelines with Docker and deploy edge-optimized TensorFlow Lite models via OTA updates to constrained IoT gateways • Implement canary and blue-green deployment strategies using ArgoCD and Istio service meshes for rolling model updates • Monitor model drift with Evidently AI and trigger automated retraining pipelines through Kubeflow Pipelines and Apache Airflow DAGs
Ethics, Bias Mitigation, and Responsible AI Practices
Audit algorithmic fairness across demographic subgroups using equalized odds, demographic parity, and calibration metrics with Fairlearn • Apply differential privacy mechanisms (Laplace noise injection, gradient clipping) to protect individual-level IoT user behavioral data • Design participatory stakeholder frameworks that incorporate environmental justice principles into LCA goal and scope definitions
Industry Integration, Business Applications, and Case Studies
Model total cost of ownership (TCO) and carbon abatement curves for smart HVAC and predictive maintenance deployments • Negotiate data-sharing agreements and API contracts with OEM suppliers to enable circular economy material traceability platforms • Synthesize cross-functional business cases that align IoT AI roadmaps with CSRD reporting requirements and Science-Based Targets initiatives
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow Lite |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Kafka |
| Covered Tool / Platform | MQTT |
| Covered Tool / Platform | Docker |
| Covered Tool / Platform | Kubernetes |
| Covered Tool / Platform | Kubeflow |
| Covered Tool / Platform | Ray Tune |
| Covered Tool / Platform | Optuna |
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