AI/ML PipeLine

Event Overview: The AI/ML Pipeline Design Challenge is a competitive, fast-paced event aimed at testing participants’ ability to design a complete, real-world AI/ML solution pipeline within a constrained time frame. Participants will work in teams of three to conceptualize and present a full end-to-end AI/ML system design, starting from problem understanding to production-level deployment. The focus is not on coding but on technical thinking, architecture design, and decision-making. Important Rule: Use of AI tools (such as ChatGPT, Copilot, Claude, or any generative AI platform) is strictly prohibited during the event. Participants must rely solely on their own knowledge, understanding, and team discussion. Participation Details: Team Size: 2/3 members per team Participation Mode: Pre-registered and shortlisted teams only Registration and Shortlisting Process Registration forms will be released prior to the event. Teams must apply with: Team Lead Resume  LinkedIn Profile The top 15 teams will be shortlisted based on: Technical background Experience in AI/ML Profile strength Pre-Event Briefing: A mandatory online briefing session will be conducted one day before the event. The session will cover: Event expectations Evaluation criteria Sample pipeline structure Event Format: Problem Statements A total of 15 AI/ML-based problem statements will be provided. Teams can choose any one problem statement. Domains include: Social Media Gaming Emerging Tech (2026 trends) Real-world AI applications Task Requirements: Each team must design a complete AI/ML pipeline, including: Problem Understanding Clear definition of the problem Use-case justification System Design and Architecture High-level architecture diagram Data flow and processing pipeline Tech Stack Selection Programming languages Frameworks and libraries Tools and platforms APIs (if applicable) Data Pipeline Data collection methods Data preprocessing techniques Feature engineering approach Model Design Model selection reasoning Training strategy Evaluation metrics Scalability and Performance Handling large user loads System optimization strategies Fault tolerance Deployment Strategy Cloud platform selection CI/CD pipeline Monitoring tools Production setup Testing Strategy Smoke testing Unit testing Performance testing Edge case handling SRS (Software Requirements Specification) Functional requirements Non-functional requirements SWOT Analysis Strengths Weaknesses Opportunities Threats Reasoning and Justification Explanation behind every technical choice Deliverables Each team must submit or present: A structured pipeline workflow in PPT format Clear explanation of: Architecture Tech stack Deployment Testing strategy Evaluation Criteria: Teams will be judged based on: Completeness of pipeline Technical depth Innovation and creativity Feasibility of implementation Clarity of explanation Justification of decisions Objective of the Event Encourage practical thinking in AI/ML system design Promote teamwork and innovation Simulate real-world product development scenarios Provide hands-on exposure to end-to-end pipeline planning