Team Persuasion PowerPoint Presentation
Budget: $750 – $1,500 CAD
ML Platform for Real-Time CTR Prediction (AdTech)
Objective
Design a scalable ML platform for real-time Click-Through Rate (CTR)
prediction in an AdTech environment. Your solution should address:
● Real-time CTR prediction for programmatic advertising
● Scalable model training, deployment, and monitoring
● Feature engineering strategies for user behavior & ad performance
● Optimized inference with latency constraints (<10ms)
● A/B testing & continuous model improvements
Scope & Key Areas to Address
Understanding the Problem: Real-Time CTR Prediction in AdTech
● Explain why CTR prediction is critical in real-time bidding (RTB) and ad targeting.
● Outline challenges in CTR modeling
Deliverable:
● Problem Definition Slide outlining key challenges in CTR prediction.
ML Techniques for CTR Prediction
Discuss ML techniques commonly used in CTR prediction
?
Deliverable:
● Slide comparing different ML models for CTR prediction.
ML Platform Architecture & Infrastructure
● Describe the ML Platform architecture:
○ Key components (Feature Store, Model Registry, CI/CD Pipelines, Model
Deployment).
○ Infrastructure choices (Cloud-based, hybrid, Kubernetes, AWS SageMaker).
● Explain data ingestion & real-time processing strategies.
● Define ML pipeline automation for training, validation, and deployment.
Deliverable:
● Architecture Diagram showing platform components & workflow.
Model Deployment & Inference
● How would you deploy models for low-latency predictions?
○ SageMaker Endpoints, Triton Inference Server, or custom APIs?
● How would you optimize inference for speed & efficiency?
Deliverable:
● Slide on model deployment strategies with latency considerations.
Monitoring & Auto-Retraining
● How do you monitor ML models in production?
○ Model drift detection, real-time performance tracking.
● What triggers a new model training pipeline?
Deliverable:
● Monitoring Pipeline Diagram + KPIs for CTR model evaluation.
Submission Guidelines
● Format: PowerPoint (PPT) or Google Slides
● Duration: 30-minute presentation + 15-minute Q&A
● Evaluation Criteria: Clarity, depth, feasibility, innovation, and presentation skills
Objective
Design a scalable ML platform for real-time Click-Through Rate (CTR)
prediction in an AdTech environment. Your solution should address:
● Real-time CTR prediction for programmatic advertising
● Scalable model training, deployment, and monitoring
● Feature engineering strategies for user behavior & ad performance
● Optimized inference with latency constraints (<10ms)
● A/B testing & continuous model improvements
Scope & Key Areas to Address
Understanding the Problem: Real-Time CTR Prediction in AdTech
● Explain why CTR prediction is critical in real-time bidding (RTB) and ad targeting.
● Outline challenges in CTR modeling
Deliverable:
● Problem Definition Slide outlining key challenges in CTR prediction.
ML Techniques for CTR Prediction
Discuss ML techniques commonly used in CTR prediction
?
Deliverable:
● Slide comparing different ML models for CTR prediction.
ML Platform Architecture & Infrastructure
● Describe the ML Platform architecture:
○ Key components (Feature Store, Model Registry, CI/CD Pipelines, Model
Deployment).
○ Infrastructure choices (Cloud-based, hybrid, Kubernetes, AWS SageMaker).
● Explain data ingestion & real-time processing strategies.
● Define ML pipeline automation for training, validation, and deployment.
Deliverable:
● Architecture Diagram showing platform components & workflow.
Model Deployment & Inference
● How would you deploy models for low-latency predictions?
○ SageMaker Endpoints, Triton Inference Server, or custom APIs?
● How would you optimize inference for speed & efficiency?
Deliverable:
● Slide on model deployment strategies with latency considerations.
Monitoring & Auto-Retraining
● How do you monitor ML models in production?
○ Model drift detection, real-time performance tracking.
● What triggers a new model training pipeline?
Deliverable:
● Monitoring Pipeline Diagram + KPIs for CTR model evaluation.
Submission Guidelines
● Format: PowerPoint (PPT) or Google Slides
● Duration: 30-minute presentation + 15-minute Q&A
● Evaluation Criteria: Clarity, depth, feasibility, innovation, and presentation skills