1 Introduction
After training the machine learning model, in order to facilitate subsequent use and deployment, the model needs to be saved. The following are several common ways of saving machine learning models:
- Local saving: You can use libraries such as Python's pickle or joblib to save the model as a file (usually with the extension .pkl, .dat), and then load the file where the model needs to be used.
- Cloud storage: Models can be saved in cloud storage, such as Amazon S3, Google Cloud Storage, etc., which is convenient for transferring, sharing and backing up models between different computers.
- Database storage: the model can be saved in the database, usually using NoSQL database such as MongoDB, Cassandra, etc., which can realize efficient data management and storage.
- Export as API: You can use web frameworks such as Flask, Django, etc. to encapsulate the model into an API interface for easy use by other applications.
- Export as a service: You can use container technologies such as Docker and Kubernetes to encapsulate the model as a microservice to achieve efficient deployment and management of the model.
When choosing a storage method, you need to choose according to the specific situation and needs. No matter which saving method is used, it is necessary to ensure that the saved model can be correctly loaded and called in subsequent use. At the same time, it is necessary to pay attention to the size and complexity of the model, as well as the security and reliability of preservation.
2. Questions
As machine learning algorithm engineers, we all know that classification, regression and other algorithms in machine learning are often used when doing modeling tasks. Well, many times we focus on the algorithm and effect of the model (academic), and few people pay attention to how to save a model after we train it, so that it can be used whenever needed . Make a summary today and share how to save the trained machine learning model (industry).