Data availability
Data availability is the extent to which required data exists and can be accessed for its intended uses when needed.
Machine Learning System Design
The condition in which required data can be accessed reliably during model training and system runtime.
- How it is used
- Ensures training and runtime pipelines can repeatedly obtain the data required for usable model predictions.
- How deep it goes
- Defines availability through ETL and serving requirements, but defers in-depth data-engineering treatment.
- Examples
- Uses third-party JSON APIs converted into CSV or Parquet, plus serving features that depend on external sources.
- What you can do after
- Operate repeatable ETL and feature pipelines while checking that training data remains obtainable during inference.
Read how Machine Learning System Design explains data availability →
Deep Learning with R, Third Edition
Data availability is the extent to which suitable examples and target annotations exist for developing a predictive model.
- How it is used
- Frames machine-learning problems by checking whether the intended prediction has usable training examples and target annotations.
- How deep it goes
- Defines the constraint and illustrates it with sentiment data, stopping at problem framing rather than detailing dataset collection or annotation procedures.
- Examples
- Uses movie reviews paired with sentiment annotations as the central example.
- What you can do after
- Evaluate whether a prediction task is learnable from existing data and recognize when collecting and annotating new examples is necessary.
Read how Deep Learning with R, Third Edition explains data availability →
Related concepts
- migrationaddressed by · A migration is a process of transferring or transforming data, software, or system state from a source form or environment to a target form or environment.
- replicationaddressed by · Replication is the creation or maintenance of multiple copies of data, state, or functionality across separate locations or components.
- prototypingmeasured by · Prototyping is the practice of creating and evaluating a preliminary or partial implementation to explore or validate an idea, design, or approach.
- pipelineconfigures · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
- language modelused by · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
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