Remove AI Tools Remove Automation Remove Data Drift
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Five open-source AI tools to know

IBM Journey to AI blog

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.

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How Quality Data Fuels Superior Model Performance

Unite.AI

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. AI-assisted dataset optimization represents another frontier.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

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

The MLOps Blog

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.