- Statistics for Machine Learning
- Pratap Dangeti
- 300字
- 2021-07-02 19:05:54
Steps in machine learning model development and deployment
The development and deployment of machine learning models involves a series of steps that are almost similar to the statistical modeling process, in order to develop, validate, and implement machine learning models. The steps are as follows:
- Collection of data: Data for machine learning is collected directly from structured source data, web scrapping, API, chat interaction, and so on, as machine learning can work on both structured and unstructured data (voice, image, and text).
- Data preparation and missing/outlier treatment: Data is to be formatted as per the chosen machine learning algorithm; also, missing value treatment needs to be performed by replacing missing and outlier values with the mean/median, and so on.
- Data analysis and feature engineering: Data needs to be analyzed in order to find any hidden patterns and relations between variables, and so on. Correct feature engineering with appropriate business knowledge will solve 70 percent of the problems. Also, in practice, 70 percent of the data scientist's time is spent on feature engineering tasks.
- Train algorithm on training and validation data: Post feature engineering, data will be divided into three chunks (train, validation, and test data) rather than two (train and test) in statistical modeling. Machine learning are applied on training data and the hyperparameters of the model are tuned based on validation data to avoid overfitting.
- Test the algorithm on test data: Once the model has shown a good enough performance on train and validation data, its performance will be checked against unseen test data. If the performance is still good enough, we can proceed to the next and final step.
- Deploy the algorithm: Trained machine learning algorithms will be deployed on live streaming data to classify the outcomes. One example could be recommender systems implemented by e-commerce websites.
推薦閱讀
- Practical Windows Forensics
- YARN Essentials
- TypeScript圖形渲染實戰:基于WebGL的3D架構與實現
- 假如C語言是我發明的:講給孩子聽的大師編程課
- Mastering Google App Engine
- Kinect for Windows SDK Programming Guide
- 實戰Java高并發程序設計(第3版)
- 從Excel到Python:用Python輕松處理Excel數據(第2版)
- Java 從入門到項目實踐(超值版)
- Instant Automapper
- JavaScript Unit Testing
- Beginning PHP
- Building Web and Mobile ArcGIS Server Applications with JavaScript(Second Edition)
- 第五空間戰略:大國間的網絡博弈
- PHP程序設計高級教程