Table of Contents

Interface IModelSerializer

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Defines methods for converting machine learning models to and from binary data for storage or transmission.

public interface IModelSerializer

Remarks

This interface provides functionality to save trained models to binary format and load them back. Serialization allows models to be stored to disk, transmitted over networks, or embedded in applications.

For Beginners: Think of serialization like taking a snapshot of your model.

Imagine you've spent hours training a machine learning model:

  • Serialization is like taking a photo of your model's current state
  • This "photo" (binary data) can be saved to your computer
  • Later, you can use this "photo" to recreate the exact same model
  • You don't need to train the model again - it's ready to use immediately

Real-world examples:

  • Saving a trained model to use it in a mobile app
  • Sharing your trained model with colleagues
  • Backing up your model before experimenting with changes
  • Deploying your model to a production environment

Without serialization, you would need to retrain your model from scratch every time you restart your application, which could take hours or days depending on the complexity of your model and data.

Methods

Deserialize(byte[])

Loads a previously serialized model from binary data.

void Deserialize(byte[] data)

Parameters

data byte[]

The byte array containing the serialized model data.

Remarks

This method takes binary data created by the Serialize method and uses it to restore a model to its previous state.

For Beginners: This is like opening a saved file to continue your work.

When you call this method:

  • You provide the binary data (bytes) that was previously created by Serialize
  • The model rebuilds itself using this data
  • After deserializing, the model is exactly as it was when serialized
  • It's ready to make predictions without needing to be trained again

For example:

  • You download a pre-trained model file for detecting spam emails
  • You deserialize this file into your application
  • Immediately, your application can detect spam without any training
  • The model has all the knowledge that was built into it by its original creator

This is particularly useful when:

  • You want to use a model that took days to train
  • You need to deploy the same model across multiple devices
  • You're creating an application that non-technical users will use

Think of it like installing the brain of a trained expert directly into your application.

LoadModel(string)

Loads the model from a file.

void LoadModel(string filePath)

Parameters

filePath string

The path to the file containing the saved model.

Remarks

This method provides a convenient way to load a model directly from disk. It combines file I/O operations with deserialization.

For Beginners: This is like clicking "Open" in a document editor. Instead of manually reading from a file and then calling Deserialize(), this method does both steps for you.

Exceptions

FileNotFoundException

Thrown when the specified file does not exist.

IOException

Thrown when an I/O error occurs while reading from the file or when the file contains corrupted or invalid model data.

SaveModel(string)

Saves the model to a file.

void SaveModel(string filePath)

Parameters

filePath string

The path where the model should be saved.

Remarks

This method provides a convenient way to save the model directly to disk. It combines serialization with file I/O operations.

For Beginners: This is like clicking "Save As" in a document editor. Instead of manually calling Serialize() and then writing to a file, this method does both steps for you.

Exceptions

IOException

Thrown when an I/O error occurs while writing to the file.

UnauthorizedAccessException

Thrown when the caller does not have the required permission to write to the specified file path.

Serialize()

Converts the current state of a machine learning model into a binary format.

byte[] Serialize()

Returns

byte[]

A byte array containing the serialized model data.

Remarks

This method captures all the essential information about a trained model and converts it into a sequence of bytes that can be stored or transmitted.

For Beginners: This is like exporting your work to a file.

When you call this method:

  • The model's current state (all its learned patterns and parameters) is captured
  • This information is converted into a compact binary format (bytes)
  • You can then save these bytes to a file, database, or send them over a network

For example:

  • After training a model to recognize cats vs. dogs in images
  • You can serialize the model to save all its learned knowledge
  • Later, you can use this saved data to recreate the model exactly as it was
  • The recreated model will make the same predictions as the original

Think of it like taking a snapshot of your model's brain at a specific moment in time.

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