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 offers a deep dive into using Google Cloud Platform to leverage cloud computing for artificial intelligence, covering from basic setup to advanced machine learning implementations and secure cloud operations. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This program offers a deep dive into using Google Cloud Platform to leverage cloud computing for artificial intelligence, covering from basic setup to advanced machine learning implementations and secure cloud operations.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
Google Cloud Foundations
• Projects, IAM and organisation policy structure
• VPC design, private access and data locality
• Billing controls and committed-use planning for accelerator workloads
BigQuery and the Analytics Layer
• BigQuery for large-scale analytical queries and cost-aware SQL
• BigQuery ML for models trained where the data already lives
• Dataflow and Pub/Sub for streaming feature pipelines
Managed Training and Serving
• Vertex AI training, pipelines and experiment tracking
• Feature Store and Model Registry in a production workflow
• Endpoint deployment, autoscaling and traffic splitting
Foundation Models on Vertex
• Model Garden, Gemini models and grounding with enterprise data
• Vector search for retrieval-augmented applications
• Prompt management, evaluation and safety configuration
MLOps and Compliance
• Continuous training and model monitoring for skew and drift
• Artifact lineage and reproducibility across environments
• Access control, audit logging and regulatory evidence
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | SPSS |
| Covered Tool / Platform | SAS |
| Covered Tool / Platform | REDCap |
| Covered Tool / Platform | Medidata Rave |
| Covered Tool / Platform | Oracle Clinical |
| Covered Tool / Platform | ClinicalTrials.gov |
Frequently Asked Questions
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