Auto-classification Data Drift LLM 
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.
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.
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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.
The MLOps Blog
MARCH 12, 2024
TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. Prompt-response management: Refining LLM-backed applications through continuous prompt-response optimization and quality control.
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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