IBM Journey to AI blog
DECEMBER 15, 2023
By using open-source AI, organizations effectively gain access to a large, diverse community of developers who constantly contribute to the ongoing development and improvement of AI tools. This collaborative environment fosters transparency and continuous improvement, leading to feature-rich, reliable and modular tools.
Unite.AI
DECEMBER 27, 2024
Data validation frameworks play a crucial role in maintaining dataset integrity over time. Automated tools such as TensorFlow Data Validation (TFDV) and Great Expectations help enforce schema consistency, detect anomalies, and monitor data drift.
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The MLOps Blog
JUNE 27, 2023
Some popular data quality monitoring and management MLOps tools available for data science and ML teams in 2023 Great Expectations Great Expectations is an open-source library for data quality validation and monitoring. It could help you detect and prevent data pipeline failures, data drift, and anomalies.
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
LLMOps (Large Language Model Operations) focuses on operationalizing the entire lifecycle of large language models (LLMs), from data and prompt management to model training, fine-tuning, evaluation, deployment, monitoring, and maintenance. LLMOps is key to turning LLMs into scalable, production-ready AI tools.
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