Optimization of machine learning models for real-time prediction
Table Of Contents
<p>1. Introduction<br> - Importance of real-time prediction in various domains<br> - Objectives of the project<br>2. Literature Review<br> - Overview of machine learning models for time-series prediction<br> - Existing approaches to real-time prediction and their limitations<br>3. Model Optimization Techniques<br> - Model compression and quantization<br> - Parallel processing and distributed computing<br> - Online learning and adaptive algorithms<br>4. Implementation<br> - Selection of machine learning frameworks and libraries<br> - Integration of optimization techniques with predictive models<br>5. Evaluation<br> - Performance metrics for prediction accuracy and latency<br> - Benchmarking against unoptimized models<br></p>
Thesis Abstract
<p> This project aims to optimize machine learning models for real-time prediction of time-series data, such as stock prices, weather forecasts, and traffic patterns. The project will focus on improving the efficiency and accuracy of predictive models by leveraging techniques such as model compression, parallel processing, and online learning. The optimized models will enable faster and more responsive predictions, with potential applications in finance, transportation, and meteorology. <br></p>Thesis Overview
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