| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $124 |
| Tools | Hugging Face Transformers TRL OpenAI Gym PPO Label Studio Prodigy Anthropic HH-RLHF Python PyTorch |
About the Human-in-the-Loop: AI Training and RLHF Course
Human-in-the-Loop: AI Training and RLHF is a cutting-edge course that focuses on the crucial role of human feedback in enhancing AI performance, safety, and ethical behavior.
As models become more autonomous and powerful (e.g., LLMs, recommendation engines), aligning their behavior with human expectations is essential. This program explores the theory and application of RLHF, HITL data annotation cycles, reward modeling, and feedback loop design—enabling participants to build scalable and robust AI systems with meaningful human oversight.
Program Highlights
• Comprehensive coverage of Human from fundamentals to advanced applications
• Hands-on projects and real-world case studies in AI
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Hugging Face Transformers, TRL, OpenAI Gym, PPO
• Career-oriented training for academic and professional growth in AI
Course Curriculum
Module 1: Understanding Human-in-the-Loop (HITL) Systems
- Define core principles of Human-in-the-Loop Learning and its role in modern AI pipelines
- Analyze the role of humans in model training, testing, and continuous monitoring workflows
- Compare feedback modalities including labels, rankings, preferences, and corrections
Module 2: Introduction to RLHF (Reinforcement Learning from Human Feedback)
- Evaluate why traditional supervised learning falls short for complex AI alignment tasks
- Identify core components of RLHF pipelines and their interdependencies
- Examine real-world examples including GPT alignment, code assistants, and human evaluation
Module 3: Collecting and Using Human Feedback
- Design effective annotation interfaces and comprehensive task guidelines for labelers
- Implement labeler training, calibration protocols, and bias reduction strategies
- Apply ranking, preference comparison, and paired evaluation techniques for quality feedback
Module 4: Reward Modeling and Fine-Tuning
- Build robust reward models from aggregated human feedback signals
- Execute fine-tuning with PPO (Proximal Policy Optimization) for policy improvement
- Align LLMs with RLHF objectives while balancing human control and model capability
Module 5: Operationalizing HITL at Scale
- Deploy Human-in-the-Loop workflows in production AI environments
- Leverage active learning and iterative retraining for continuous model improvement
- Integrate APIs, dashboards, and automated feedback loops for scalable operations
Module 6: Governance, Safety, and the Future of Human Feedback
- Assess limitations and risks inherent in RLHF implementations
- Navigate ethical and legal considerations in HITL system design
- Balance human-AI collaboration with appropriate control mechanisms
Module 7: Capstone Project – Building an RLHF-Aligned System
- Architect end-to-end RLHF pipelines from feedback collection to model deployment
- Validate system performance against safety, helpfulness, and harmlessness criteria
- Present solutions to expert panel for feedback and industry readiness assessment
Tools, Techniques, or Platforms Covered
Hugging Face Transformers TRL OpenAI Gym PPO Label Studio Prodigy Anthropic HH-RLHF Python PyTorch
Real-World Applications
- Apply Human skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical AI competencies
- Solve industry-relevant problems using Human methodologies and tools
- Contribute to open-source projects and collaborative research in AI
- Prepare for competitive examinations, interviews, and professional certifications in AI
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NSTC
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
