AI5 PROFESSIONAL CERTIFICATE · BY TGC INDIA · UPDATED AUGUST 2026
Responsible AI & Model Evaluation
Assess bias, quality, safety, drift and responsible use of AI systems.
Online + OfflineWeekdays + WeekendsRegular + Fast Track
22+years in training
20,000+learners across TGC
5classroom locations
Live onlinejoin from anywhere
NEXT START OPTIONS
Choose a live batch.
COURSE OVERVIEW
What this course
is built to do.
Test AI systems for quality, fairness, safety and drift, then prepare clear release and monitoring records.
Each module combines a live trainer demonstration, guided lab, assignment and review before the next stage.
CONNECTED CAREER DIRECTIONS
Responsible AI AnalystAI Evaluation SpecialistModel Risk AssociateAI Governance Coordinator
IS THIS COURSE RIGHT FOR YOU?
Choose it for the right reason.
You build or approve AI systems
You need fairness and safety tests
You want documented release and monitoring decisions
YOUR LEARNING ARC
From guided foundation to finished work.
Scope
Define purpose, users, harms and measures.
Test
Check data quality, fairness and task success.
Attack
Evaluate hallucination, misuse and unsafe responses.
Govern
Prepare release records and post-launch monitoring.
INDUSTRY TASKS
Practise the work, not only the tool.
- Run a segmented fairness evaluation
- Build a generative-AI test suite
- Prepare a model card and launch decision
PRO
LED
LED
YOUR TRAINING TEAM
Learn from experienced working professionals.
Live demonstrations, guided practice and direct project feedback are part of the course. Trainers update examples and tool coverage as professional practice changes.
WHAT YOU WILL BE ABLE TO DO
Course outcomes
01
Plan responsible AI evaluation
A purpose, risk, metric and ownership charter.
02
Test fairness and data quality
A segmented performance and mitigation report.
03
Evaluate generative AI systems
A test suite covering quality, groundedness and attacks.
04
Support release and monitoring decisions
A model card, risk register and post-launch plan.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
DETAILED COURSE FLOW
5 learning modules
Assess model quality, bias, safety and drift through test sets, evidence records and clear deployment decisions.
Responsible AI and evaluation planning
Define the intended use, affected users and evidence required before release.
CORE TOPICS
- System purpose and use boundaries
- Stakeholders and possible harms
- Quality, fairness and safety goals
- Evaluation plan and decision owners
GUIDED PRACTICE
Create a risk-and-evaluation plan for a selected AI system.
MODULE DELIVERABLE
Responsible AI evaluation charter
Data quality and fairness testing
Check whether data and model performance differ across relevant groups.
CORE TOPICS
- Sampling, labels and representation
- Missing and proxy variables
- Group performance and fairness measures
- Fairlearn and mitigation options
GUIDED PRACTICE
Run a segmented evaluation and document performance gaps and data limits.
MODULE DELIVERABLE
Fairness and data-quality report
Generative AI and safety evaluation
Test language-model applications for accuracy, harmful output and misuse.
CORE TOPICS
- Task success and groundedness
- Hallucination and citation checks
- Prompt attacks and policy tests
- Human scoring and automated evals
GUIDED PRACTICE
Build and run a test set covering normal, difficult and hostile requests.
MODULE DELIVERABLE
Generative AI evaluation suite
Model cards, governance and release
Turn technical results into clear release, restriction or stop decisions.
CORE TOPICS
- Model and system cards
- Risk register and mitigation tracking
- Approval, audit and documentation
- Privacy, copyright and regulatory awareness
GUIDED PRACTICE
Prepare release documentation with use limits, controls and unresolved risks.
MODULE DELIVERABLE
Model card and release dossier
Monitoring, drift and evaluation capstone
Continue checking the system after release and respond to change.
CORE TOPICS
- Data and concept drift
- Quality and safety monitoring
- Incident reporting and rollback
- Review cycle and update ownership
GUIDED PRACTICE
Present an end-to-end evaluation with launch decision and monitoring plan.
MODULE DELIVERABLE
Responsible AI capstone
HOW THE TRAINING WORKS
Learn it. Apply it. Get it reviewed. Improve it.
Every important skill moves through explanation, demonstration, guided use and independent application. Trainer feedback is used to revise the work before it becomes part of the final portfolio.
Concept briefing
The trainer explains the principle, use case, limitations and the quality standard expected.
Live demonstration
A complete task is demonstrated while the trainer explains decisions, checks and common mistakes.
Guided lab
Learners repeat the method with support, ask questions and correct problems during the session.
Applied assignment
The same method is used on a different brief so the learner must make independent decisions.
Review and revision
Work is checked against a rubric, revised after feedback and prepared for project presentation.
PROGRESS IS CHECKED THROUGHClass exercisesModule deliverablesProject reviewsFinal capstone presentation
PORTFOLIO WORK
Projects you can show
Guided practice brief
Plan, produce, test and present a finished piece with trainer feedback.
Individual application
Plan, produce, test and present a finished piece with trainer feedback.
Workflow build
Plan, produce, test and present a finished piece with trainer feedback.
Industry-style assignment
Plan, produce, test and present a finished piece with trainer feedback.
Quality review
Plan, produce, test and present a finished piece with trainer feedback.
Final capstone
Plan, produce, test and present a finished piece with trainer feedback.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
TOOLS COVERED
PythonEvalsModel CardsFairlearn
Tool coverage may be updated when the industry changes. Core methods remain part of the course.COMMON QUESTIONS
Before you apply
Is this only for data scientists?
No. Developers, product, risk and governance teams can join if they understand the AI system being assessed.
＋Are practical tools included?
Yes. Learners use Python evaluation libraries and structured human review methods.
＋Does it cover post-launch drift?
Yes. Data change, model quality, incident response and rollback form part of the final module.
＋Do I need coding experience?
No, unless the course level says otherwise. Your advisor will check the right starting level.
＋Are classes live or recorded?
Classes are trainer-led in the classroom or live online. Recordings may support revision but do not replace class.
＋Will I receive a certificate?
Yes. Course completion requires attendance, assignments and the final project.
＋Can working professionals join?
Yes. Weekday, weekend and selected fast-track schedules are available.
＋NEXT BATCH
Choose your course.
Choose your schedule.
Online or offline. Weekdays or weekends. Regular or fast track. Speak with an AI5 Academy advisor about the right starting level.