Showing 3 open source projects for "log analysis tools"

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    FiftyOne

    FiftyOne

    The open-source tool for building high-quality datasets

    ...FiftyOne provides the building blocks for optimizing your dataset analysis pipeline. Use it to get hands-on with your data, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more! Surveys show that machine learning engineers spend over half of their time wrangling data, but it doesn't have to be that way.
    Downloads: 0 This Week
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  • 2
    iTop - IT Service Management & CMDB

    iTop - IT Service Management & CMDB

    An easy, extensible web based IT service management platform

    Whether you’re an infrastructure manager handling complex systems, a service support leader striving for customer satisfaction, or a decision-maker focused on ROI and compliance, iTop adapts to your processes to simplify your tasks, streamline operations, and enhance service quality. iTop (IT Operations Portal) by Combodo is an all-in-one, open-source ITSM platform designed to streamline IT operations. iTop offers a highly customizable, low-code Configuration Management Database (CMDB),...
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    Downloads: 867 This Week
    Last Update:
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  • 3
    WhyLogs Java Library

    WhyLogs Java Library

    Profile and monitor your ML data pipeline end-to-end

    ...Understanding the properties of data as it moves through applications is essential to keeping your ML/AI pipeline stable and improving your user experience, whether your pipeline is built for production or experimentation. WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log files that can be used for monitoring, alerts, analytics, and error analysis. WhyLogs calculates approximate statistics for datasets of any size up to TB-scale, making it easy for users to identify changes in the statistical properties of a model's inputs or outputs. Using approximate statistics allows the package to run on minimal infrastructure and monitor an entire dataset, rather than miss outliers and other anomalies by only using a sample of the data to calculate statistics.
    Downloads: 0 This Week
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