Unite.AI
JUNE 24, 2024
This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call data drift. There is only one way to identify the data drift, by continuously monitoring your models in production.
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.
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The MLOps Blog
JUNE 27, 2023
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. The platform provides a comprehensive set of annotation tools, including object detection, segmentation, and classification.
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.
DataRobot Blog
MARCH 10, 2022
By enabling data scientists to rapidly iterate through model development, validation, and deployment, DataRobot provides the tools to blitz through steps four and five of the machine learning lifecycle with AutoML and Auto Time-Series capabilities. and recommend the best optimization metric to use.
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
The manual collection of training data for Text2SQL is particularly tedious. It not only requires SQL mastery on the part of the annotator, but also more time per example than more general linguistic tasks such as sentiment analysis and text classification. 3] provides a more complete survey of Text2SQL data augmentation techniques.
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