Showing 3 open source projects for "quality management"

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  • 1
    Pandas Profiling

    Pandas Profiling

    Create HTML profiling reports from pandas DataFrame objects

    pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,...
    Downloads: 6 This Week
    Last Update:
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  • 2
    Arize Phoenix

    Arize Phoenix

    Uncover insights, surface problems, monitor, and fine tune your LLM

    Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)...
    Downloads: 3 This Week
    Last Update:
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  • 3
    Colibrì

    Colibrì

    Run frontier MoE models on hardware you already own

    Colibrì is an open-source inference engine designed to run frontier-scale mixture-of-experts (MoE) models on consumer and heterogeneous hardware. It treats VRAM, system RAM, and NVMe storage as a unified memory hierarchy, dynamically placing and streaming model weights where they can be accessed most efficiently. This architecture enables users to run models ranging from 7 billion to 2.8 trillion parameters without requiring the entire model to fit in expensive GPU memory. Written primarily...
    Downloads: 73 This Week
    Last Update:
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