Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
Explore the next generation of Operational Technology (OT 2.0) where AI co‑pilots support predictive maintenance, anomaly detection, and live governance of industrial processes. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Explore the next generation of Operational Technology (OT 2.0) where AI co‑pilots support predictive maintenance, anomaly detection, and live governance of industrial processes.
📋 Course Objectives
1. Apply Artificial Intelligence methods to authentic research and industry problems.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• Tangible, reproducible Artificial Intelligence work to show supervisors or employers.
• 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
Day 1 – AI Co‑Pilot & Prompt Architecture
Understand foundations of LLMs for OT • Query telemetry data and parse error logs in OpenAI Playground • Design role‑based and few‑shot prompts • Build a custom troubleshooting bot using Flowise/OpenAI GPTs
Day 2 – No‑Code Machine Learning for Predictive Maintenance
Compare supervised vs. unsupervised learning for asset management • Train a failure classification model with BigML/MindsDB • Interpret confusion matrices, accuracy, and false‑positive rates • Run predictive regressions on energy and supply‑chain costs via Julius AI
Day 3 – AI Governance, Risk & Stress‑Testing
Evaluate data sovereignty for public vs. hybrid AI deployments • Conduct prompt injection, red‑team, and vulnerability testing with Giskard/Vellum • Apply techniques for explainable automated decision‑making • Design Human‑in‑the‑Loop validation checkpoints
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | OpenAI Playground |
| Covered Tool / Platform | Flowise |
| Covered Tool / Platform | BigML |
| Covered Tool / Platform | MindsDB |
| Covered Tool / Platform | Julius AI |
| Covered Tool / Platform | Giskard |
| Covered Tool / Platform | Vellum |
| Covered Tool / Platform | Airtable |
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