Article by Ayman Alheraki in January 24 2025 01:40 PM
In the field of machine learning, training and data storage differ significantly from traditional database systems. Here’s an explanation of the process, along with tools commonly used and how trained data is handled within machine learning models.
In machine learning, "training" refers to teaching a model to perform a specific task, such as recognizing images, understanding text, or making predictions based on new inputs.
Training involves feeding the model with data inputs (e.g., text, images, sounds) so it can analyze and extract patterns.
The goal is for the model to generalize and make accurate predictions when presented with new, unseen data.
Unlike traditional databases where data is stored in an accessible format for direct queries, trained data is stored in a very different manner:
Weights: During training, the model updates certain values called "weights," which determine the model’s response to specific inputs.
Neural Network: These weights are stored within layers of artificial neurons. The weights themselves represent the trained data, but they are not directly readable or accessible, as they rely on mathematical interactions within the network.
The weights are usually stored in model files in formats like HDF5 or Checkpoints or other specific formats based on the library used, such as TensorFlow or PyTorch. These files contain all the numeric values the model learned from the training process, which are typically large because each layer and neuron contain a set of weights.
Once training is complete, the model shifts to an inference phase, allowing it to:
Predict or Classify: Receive new inputs (e.g., text or images) and produce outputs based on learned information.
Make Decisions: The model can evaluate and generate content (e.g., in generative AI like ChatGPT, where it forms responses based on trained data).
Several tools and libraries are essential in machine learning training and weight storage, including:
TensorFlow: An open-source library from Google for training models and storing weights.
PyTorch: A popular library from Facebook for model training and result storage.
Hugging Face Transformers: A specialized library for language models, containing popular models like GPT and BERT, used for data processing and model training.
Although machine learning models don’t depend directly on databases for training, databases play a crucial role in:
Storing Raw Data: Data for training, like text, images, or transactional logs.
Caching Inputs and Outputs: In applications that require repetitive processes like user queries, fast databases like Redis can temporarily store results.
Imagine a model trained on text about cars. During training, the model analyzes millions of sentences related to cars, extracting common patterns:
Words are converted to numerical representations and processed through the neural network.
Once training concludes, the model is saved in a file containing learned weights.
When a new question about cars is presented, the model loads its stored weights and uses them to generate a response based on previously learned patterns.