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Data Quality

What does high quality data for ML look like?

High data quality for ML refers to data that can be used to train high performance models. Poor training data quality for ML results in models that have low performance, are biased, and cannot generalize.

Some important properties of data for it to be considered high quality include its accuracy, consistency, and level-of-noise. For the task the model is being trained for, the data should also be relevant, complete, timely, representative, and unbiased. High-quality data is essential for building robust and reliable models that can generate accurate predictions or perform desired tasks effectively.

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AI Lakehouse
AI Pipeline
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AutoML
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Backfill features
Backfill training data
Backpressure for feature stores
Batch Inference Pipeline
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CI/CD for MLOps
Compound AI Systems
Context Window for LLMs
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DAG Processing Model
Data Compatibility
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KServe
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SQL UDF in Python
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Splitting Training Data
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Upstream
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Validation Set
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