Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This program provides a comprehensive introduction to using Microsoft Azure’s vast suite of AI services and computing resources to build, deploy, and manage AI applications across different sectors. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This program provides a comprehensive introduction to using Microsoft Azure’s vast suite of AI services and computing resources to build, deploy, and manage AI applications across different sectors.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology 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
Azure Foundations for AI Workloads
• Subscriptions, resource groups and role-based access control
• Networking, private endpoints and data residency choices
• Cost management and the levers that actually control AI spend
Azure AI Services
• Vision, speech, language and document intelligence services
• Azure OpenAI Service: deployment, quotas and content filtering
• When a prebuilt service is sufficient and when it becomes a constraint
Azure Machine Learning
• Workspaces, compute targets and managed endpoints
• Pipelines, environments and model registry
• Responsible AI dashboard components for fairness and error analysis
Grounding and Retrieval
• Azure AI Search for retrieval-augmented generation
• Data ingestion, chunking and index maintenance
• Evaluating retrieval quality rather than assuming it
Production and Governance
• Monitoring, logging and drift detection for deployed endpoints
• CI/CD for models with Azure DevOps or GitHub Actions
• Security posture, identity and compliance evidence for enterprise deployment
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
