1. Introduction
  - Importance of real-time prediction in various domains
  - Objectives of the project
2. Literature Review
  - Overview of machine learning models for time-series prediction
  - Existing approaches to real-time prediction and their limitations
3. Model Optimization Techniques
  - Model compression and quantization
  - Parallel processing and distributed computing
  - Online learning and adaptive algorithms
4. Implementation
  - Selection of machine learning frameworks and libraries
  - Integration of optimization techniques with predictive models
5. Evaluation
  - Performance metrics for prediction accuracy and latency
  - Benchmarking against unoptimized models