Auto-classification Auto-complete Data Drift 
AWS Machine Learning Blog
MARCH 1, 2023
Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.
ODSC - Open Data Science
OCTOBER 11, 2023
Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.
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The MLOps Blog
JUNE 27, 2023
Can you see the complete model lineage with data/models/experiments used downstream? Amazon SageMaker Ground Truth SageMaker Ground Truth is a fully managed data labeling service designed to help you efficiently label and annotate your training data with high-quality annotations. Can you render audio/video?
The MLOps Blog
MARCH 12, 2024
Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools. It involves transforming textual data into numerical form, known as embeddings, representing the semantic meaning of words, sentences, or documents in a high-dimensional vector space.
Topbots
JUNE 29, 2023
On a more advanced stance, everyone who has done SQL query optimisation will know that many roads lead to the same result, and semantically equivalent queries might have completely different syntax. The manual collection of training data for Text2SQL is particularly tedious.
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