Deploying Machine Learning Models: A Step-by-Step Tutorial
Which formats are used for model serialization?
Model serialization formats include JSON, XML, HDF5, and Python-specific formats such as Pickle, Joblib, and ONNX. These formats allow for the conversion of a data object into a format that can be stored or transmitted and then recreated when needed through deserialization2.
How is data preprocessing handled in model deployment?
Data preprocessing in model deployment involves handling missing values, encoding categorical variables, and normalizing/standardizing numerical features. Techniques such as mean imputation, one-hot encoding, and standardization are commonly used. This step is crucial to ensure the model's predictive ability and generalizability to new data.
What is model deployment in machine learning?
Model deployment in machine learning refers to the process of integrating a trained machine learning model into an existing production environment, where it can take in input data and provide predictions or categorizations for practical applications16. It involves setting up the necessary infrastructure, packaging the model, and ensuring proper monitoring and maintenance for its continuous use.