Search Results for "metrics" - Page 2
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Showing 240 open source projects for "metrics"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Cloud Custodian
Rules engine for cloud security, cost optimization, and governance
...The simple YAML DSL allows you to easily define rules to enable a well-managed cloud infrastructure, that's both secure and cost-optimized. It consolidates many of the ad-hoc scripts organizations have into a lightweight and flexible tool, with unified metrics and reporting. Custodian supports managing AWS, Azure, and GCP public cloud environments. Besides just providing reports of issues, Custodian can actively enforce the security policies you define. Setup off-hours to save you money, by turning-off resources when they're not being used. Garbage collects unused resources by looking into utilization metrics. ... -
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HolmesGPT
CNCF Sandbox Project
HolmesGPT is an open-source AI agent designed to help DevOps and site reliability engineering teams diagnose and resolve production incidents. The system aggregates signals from observability tools such as logs, metrics, alerts, and distributed traces, then analyzes them using large language models to identify potential root causes. Rather than requiring engineers to manually correlate large volumes of monitoring data, HolmesGPT automatically synthesizes evidence and presents explanations in natural language. The project is developed by Robusta and has been accepted as a Cloud Native Computing Foundation Sandbox project, highlighting its relevance to the cloud-native ecosystem. ... -
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Fara-7B
An Efficient Agentic Model for Computer Use
...It provides stakeholders with a way to benchmark and evaluate models across dimensions such as fairness, robustness, security, privacy, and ethical considerations. Rather than relying on ad-hoc or manual review processes, FARA enables organizations to profile AI behavior using standardized tests, metrics, and reporting templates, making evaluations reproducible and comparable over time. The framework supports plugin-based modules that can be tailored to industry-specific concerns or regulatory requirements, helping compliance teams, auditors, and engineers collaborate on shared assessment goals. -
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Engram
A New Axis of Sparsity for Large Language Models
...It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in mind, Engram supports batched indexing, incremental updates, and custom distance metrics so developers can tailor search behaviors to their domain’s needs. In addition to raw similarity search, the project includes tools for clustering, ranking, and filtering results, enabling richer user experiences like “related content”, semantic auto-completion, and contextual filtering. -
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Oumi
Everything you need to build state-of-the-art foundation models
Oumi is an open-source framework that provides everything needed to build state-of-the-art foundation models, end-to-end. It aims to simplify the development of large-scale machine-learning models. -
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Best-of Python Developer Tools
A ranked list of awesome python developer tools and libraries
...This curated list contains 270 awesome open-source projects with a total of 810K stars grouped into 16 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! -
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Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. ... -
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Applio
A simple, high-quality voice conversion tool focused on ease of use
...Applio is considered stable and mature; ongoing development is now centered on security patches, dependency maintenance, and occasional improvements, which makes it attractive for production or repeatable workflows. It also includes TensorBoard helper scripts so people training custom models can monitor metrics and experiment more systematically. -
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SwanLab
An open-source, modern-design AI training tracking and visualization
SwanLab is an open-source experiment tracking and visualization platform designed to help machine learning engineers monitor, compare, and analyze the training of artificial intelligence models. The tool records training metrics, hyperparameters, model outputs, and experiment configurations so that developers can easily understand how different experiments perform over time. It provides a modern user interface for visualizing results, enabling teams to compare runs, track model performance trends, and collaborate on machine learning research. SwanLab supports both cloud and self-hosted deployments, allowing organizations to run the system privately or integrate it into shared development environments. ... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Blueprint MCP
Diagram generation for understanding codebases and system architecture
Blueprint MCP is a modular control plane designed for managing and orchestrating multiple game-server clusters in real time, giving operators fine-grained control over scaling, configuration, and deployment workflows across distributed infrastructure. It provides a central management REST API and dashboard where teams can view cluster health, adjust instance fleets, set auto-scaling policies, and monitor usage metrics in a unified interface. Blueprint-MCP also supports templated server configurations so game environments can be versioned, replicated, and deployed consistently across regions or cloud providers. The control plane includes hooks for event-driven automation, allowing rules like “scale up at peak hours” or “restart unhealthy nodes automatically” to be codified and managed without manual intervention. ... -
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HunyuanWorld 1.0
Generating Immersive, Explorable, and Interactive 3D Worlds
...HunyuanWorld-1.0 surpasses existing open-source methods in visual quality and geometric consistency, demonstrated by superior scores in BRISQUE, NIQE, Q-Align, and CLIP metrics. -
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Elementary
Open-source data observability for analytics engineers
...Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. ... -
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Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
...These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. The library supports a wide variety of tasks including image classification, object detection, semantic segmentation, and similarity analysis. It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. By integrating easily with PyTorch models, the library allows developers to diagnose model errors, detect biases in datasets, and improve model transparency. -
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uqlm
Uncertainty Quantification for Language Models, is a Python package
...Black-box methods evaluate model outputs through multiple generations or comparative analysis, while white-box methods rely on token probabilities produced during inference. UQLM also supports ensemble strategies and model-as-judge approaches for evaluating responses. By combining multiple uncertainty metrics, the system provides more reliable indicators of when language model outputs may be unreliable. -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
...It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place. -
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ZeusDB Vector Database
Blazing-fast vector DB with similarity search and metadata filtering
...Hybrid search is a core design goal, allowing you to mix vector, keyword, and filter queries in a single request for practical relevance. Observability and safety round out the system, with metrics, tracing, and guardrails to manage recalls, deletions, and privacy-sensitive data at scale. -
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ContextForge MCP Gateway
A Model Context Protocol (MCP) Gateway & Registry
...Operators can define virtual servers, wire multiple transports, and optionally enable an admin UI for management and monitoring. Packaged for quick starts via PyPI and Docker, it targets production reliability with health checks, metrics, and structured logs. The project positions itself as an integration hub so agentic apps can “connect once, use many” backends with consistent policy and lifecycle control. -
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Purple Llama
Set of tools to assess and improve LLM security
...CyberSecEval, one of its flagship components, provides repeatable evaluations for security risk, including agent-oriented tasks such as automated patching benchmarks. The aim is to make safety practical: ship testable baselines, publish metrics, and provide drop-in implementations that reduce friction for teams adopting Llama. Documentation and sites attached to the repo walk through setup, usage, and the rationale behind each safeguard, encouraging community contributions. -
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
The Hunyuan3D-2 model, developed by Tencent, is designed for generating high-resolution 3D assets using large-scale diffusion models. This model offers advanced capabilities for creating detailed 3D models, including texture enhancements, multi-view shape generation, and rapid inference for real-time applications. It is particularly useful for industries requiring high-quality 3D content, such as gaming, film, and virtual reality. Hunyuan3D-2 supports various enhancements and is available... -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. -
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MLE-Agent
Intelligent companion for seamless AI engineering and research
MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. A library designed for managing machine learning experiments, tracking metrics, and model deployment. -
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MindNLP
Easy-to-use and high-performance NLP and LLM framework
MindNLP is a natural language processing library built on the MindSpore framework, providing tools and models for various NLP tasks. -
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Fish Speech
SOTA Open Source TTS
...The repository hosts the code and tooling for training, fine-tuning, and serving high-quality TTS, while the current flagship models (OpenAudio-S1 and S1-mini) are distributed via Fish Audio’s playground and Hugging Face. The models are evaluated with Seed TTS metrics and achieve exceptionally low word and character error rates, indicating strong intelligibility and alignment between text and audio. Fish Speech emphasizes expressive and controllable voices: it supports a long list of emotion tags, tone markers, and special audio effect markers that can be embedded in the text to drive prosody and vocal style, from basic emotions to nuanced states like sarcastic, conciliative, or hysterical. ... -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
...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, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). ... -
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Tracking Any Point (TAP)
DeepMind model for tracking arbitrary points across videos & robotics
TAPNet is the official Google DeepMind repository for Tracking Any Point (TAP), bundling datasets, models, benchmarks, and demos for precise point tracking in videos. The project includes the TAP-Vid and TAPVid-3D benchmarks, which evaluate long-range tracking of arbitrary points in 2D and 3D across diverse real and synthetic videos. Its flagship models—TAPIR, BootsTAPIR, and the latest TAPNext—use matching plus temporal refinement or next-token style propagation to achieve state-of-the-art...
