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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

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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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

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.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

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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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

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.

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Creating An Information Edge With Conversational Access To Data

Topbots

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.