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@arizeai/phoenix-mcp

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by Arize-ai
cookbooks.md1.81 kB
# More Cookbooks Use embeddings to explore lower-dimensional representations of your data, identifying clusters of high drift and performance degradation. Complement this with statistical analysis of structured data for A/B testing, temporal drift detection, and deeper performance insights. <table data-view="cards"><thead><tr><th></th><th></th><th></th><th></th><th data-hidden data-card-cover data-type="files"></th></tr></thead><tbody><tr><td><strong>Embeddings Analysis: Data Exploration</strong></td><td><a href="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/find_cluster_export_and_explore_with_gpt.ipynb">Find Clusters, Export, and Explore with GPT</a></td><td></td><td></td><td><a href="../.gitbook/assets/da1.avif">da1.avif</a></td></tr><tr><td><strong>Embeddings Analysis: Model Performance</strong></td><td><a href="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/image_classification_tutorial.ipynb">Active Learning for a Drifting Image Classification Model</a></td><td><a href="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/sentiment_classification_tutorial.ipynb">Root-Cause Analysis for a Drifting Sentiment Classification Model</a></td><td><a href="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/llm_summarization_tutorial.ipynb">Troubleshooting an LLM Summarization Task</a></td><td><a href="../.gitbook/assets/da2.png">da2.png</a></td></tr><tr><td><strong>Structured Data Analysis</strong></td><td><a href="https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/credit_card_fraud_tutorial.ipynb">Detecting Fraud with Tabular Embeddings</a></td><td></td><td></td><td><a href="../.gitbook/assets/da3.avif">da3.avif</a></td></tr></tbody></table>

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