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Professional software vendor delivering innovative solutions on the Softono platform. Specialized in both open-source and proprietary software development.

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Software by awesome-mlops

awesome-ml-experiment-management
Open Source

awesome-ml-experiment-management

# awesome-ml-experiment-management A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀 * [Aim](https://github.com/aimhubio/aim): An easy-to-use and performant open-source experiment tracker. * [ClearML](https://github.com/allegroai/clearml): Automagical experiment tracking, environments and results * [Comet](https://www.comet.ml/): Manage and optimize the entire ML lifecycle, from experiment tracking to model production monitoring. * [DVC Studio](https://studio.iterative.ai/): A web application that works with the data, metrics and hyperparameters that you add to your ML project repositories. * [Guild AI](https://guild.ai/): Open source experiment tracking, pipeline automation, and hyperparameter tuning. * [Keepsake](https://github.com/replicate/keepsake): Version control for machine learning with support to Amazon S3 and Google Cloud Storage. * [mlflow](https://github.com/mlflow/mlflow): Open source platform for the machine learning lifecycle. * [Neptune](https://neptune.ai/): A lightweight experiment management tool that fits any workflow. * [Polyaxon](https://github.com/polyaxon/polyaxon): Open-source ML experiemnts management platform. * [Sacred](https://github.com/IDSIA/sacred/): A tool to configure, organize, log and reproduce computational experiments. * [Tensorboard](https://www.tensorflow.org/tensorboard/): Provides the visualization and tooling needed for machine learning experimentation. * [TraceML](https://github.com/polyaxon/traceml): Engine for ML/Data tracking, visualization, dashboards, and model UI. * [Weights and Biases](https://github.com/wandb/client): A tool for visualizing and tracking your machine learning experiments.

Education & Learning
165 Github Stars
awesome-ml-monitoring
Open Source

awesome-ml-monitoring

# awesome-ml-monitoring A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀 * [Aporia](https://www.aporia.com/): Observability with customized monitoring and explainability for ML models. * [Arize](https://github.com/Arize-ai/client_python): An end-to-end ML observability and model monitoring platform. * [Datatile](https://github.com/polyaxon/datatile): A library for managing, summarizing, and visualizing data. * [DataProfiler](https://github.com/capitalone/DataProfiler): A Python library designed to make data analysis, monitoring and sensitive data detection easy. * [Deepchecks](https://github.com/deepchecks/deepchecks): Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. * [Evidently](https://github.com/evidentlyai/evidently): Interactive reports to analyze ML models during validation or production monitoring. * [Fiddler](https://www.fiddler.ai/): Monitor, explain, and analyze your AI in production. * [Great Expectations](https://github.com/great-expectations/great_expectations): Helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. * [Manifold](https://github.com/uber/manifold): A model-agnostic visual debugging tool for machine learning. * [Netron](https://github.com/lutzroeder/netron): Visualizer for neural network, deep learning, and machine learning models. * [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling): Extends the pandas DataFrame with df.profile_report() for quick data analysis. * [Pandera](https://github.com/pandera-dev/pandera): A light-weight, flexible, and expressive data validation library for dataframes. * [Superwise](https://www.superwise.ai): Fully automated, enterprise-grade model observability in a self-service SaaS platform. * [Whylogs](https://github.com/whylabs/whylogs): The open source standard for data logging. Enables ML monitoring and observability. * [ydata-quality](https://github.com/ydataai/ydata-quality): Data Quality assessment with one line of code. * [Yellowbrick](https://github.com/DistrictDataLabs/yellowbrick): Visual analysis and diagnostic tools to facilitate machine learning model selection. * [Soda Core](https://github.com/sodadata/soda-core): Data profiling, testing, and monitoring for SQL accessible data.

AI & Machine Learning Monitoring & Observability
93 Github Stars
awesome-ml-serving
Open Source

awesome-ml-serving

# awesome-ml-serving A curated list of awesome open source and commercial platforms for serving models in production 🚀 * [Banana](https://banana.dev): Host your ML inference code on serverless GPUs and integrate it into your app with one line of code. * [BentoML](https://github.com/bentoml/BentoML): Open-source platform for high-performance ML model serving. * [BudgetML](https://github.com/ebhy/budgetml): Deploy a ML inference service on a budget in less than 10 lines of code. * [Cortex](https://www.cortex.dev/): Machine learning model serving infrastructure. * [Gradio](https://github.com/gradio-app/gradio): Create customizable UI components around your models. * [GraphPipe](https://oracle.github.io/graphpipe): Machine learning model deployment made simple. * [Hydrosphere](https://github.com/Hydrospheredata/hydro-serving): Platform for deploying your Machine Learning to production. * [KFServe](https://github.com/kserve/kserve): Kubernetes custom resource definition for serving ML models on arbitrary frameworks. * [Merlin](https://github.com/gojek/merlin): A platform for deploying and serving machine learning models. * [Opyrator](https://github.com/ml-tooling/opyrator): Turns your ML code into microservices with web API, interactive GUI, and more. * [PredictionIO](https://github.com/apache/predictionio): Event collection, deployment of algorithms, evaluation, querying predictive results via APIs. * [Rune](https://github.com/hotg-ai/rune): Provides containers to encapsulate and deploy EdgeML pipelines and applications. * [Seldon](https://www.seldon.io/): Take your ML projects from POC to production with maximum efficiency and minimal risk. * [Streamlit](https://github.com/streamlit/streamlit): Lets you create apps for your ML projects with deceptively simple Python scripts. * [TensorFlow Serving](https://www.tensorflow.org/tfx/guide/serving): Flexible, high-performance serving system for ML models, designed for production. * [TorchServe](https://github.com/pytorch/serve): A flexible and easy to use tool for serving PyTorch models. * [Triton Inference Server](https://github.com/triton-inference-server/server): Provides an optimized cloud and edge inferencing solution.

ML Frameworks
51 Github Stars