// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Data Split
Dividing a dataset into different subsets, typically for training, validation, and testing machine learning models.
TECHNICAL DEFINITION
The partitioning of a dataset into distinct subsets, commonly training, validation, and test sets, to enable robust model development, hyperparameter tuning, and unbiased performance evaluation, preventing overfitting and ensuring generalization.
BACKGROUND
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Train-test split
- dataset partitioning
- data division
- cross-validation split
USAGE NOTE
A proper data split is vital for evaluating a model's ability to generalize to unseen data.
DEVELOPERS
Organizations developing technology related to Data Split.
Develops the popular 'datasets' library, which provides standardized methods for accessing and processing datasets. The library includes built-in, efficient functionalities for splitting datasets into training, validation, and test sets, a fundamental step in AI engineering.
Provides a unified data and AI platform where managing data pipelines is a core feature. Using tools like Delta Lake and MLflow, they enable versioning, tracking, and reproducible splitting of massive datasets for training machine learning models at scale.
An MLOps platform for experiment tracking. Its 'Artifacts' feature allows developers to version datasets, ensuring that the exact data splits used for training, validation, and testing are logged and reproducible for any given model.
A data-centric AI platform focused on programmatic data labeling. Snorkel Flow automates the creation of training data and includes sophisticated workflows for splitting this data to train and validate models without data leakage from the labeling functions.
Through its Vertex AI platform, Google provides managed dataset services. Users can upload data and define persistent splits (e.g., 80% training, 10% validation, 10% testing) that are then consistently used across AutoML and custom training jobs.
Offers Amazon SageMaker, a comprehensive machine learning service. SageMaker Data Wrangler and Processing jobs provide tools for developers to programmatically and visually define and execute data splitting logic as a step in a larger ML pipeline.
An enterprise AI platform that automates many aspects of the model development lifecycle. A core component of its technology is the automatic and intelligent partitioning of data, often using advanced techniques like stratified k-fold cross-validation, to ensure model robustness.
Provides a data-centric platform, the Scale Data Engine, for managing the entire AI lifecycle. The platform includes features for curating datasets, which involves creating specific splits for training, validation, and testing to accelerate model development.