Open source framework to build, share and deploy AI applications
Acumos is an open source platform, which supports design, integration and deployment of AI models. Furthermore, it offers an AI marketplace that empowers data scientists to publish adaptive AI models, while shielding them from the need to custom develop fully integrated solutions.
Open source toolkit for accelerating deep learning inference
Adlik is an end-to-end optimizing framework for deep learning models. The goal of Adlik is to accelerate deep learning inference process both on cloud and embedded environments.
Open source tools to evaluate, defend, certify and verify Machine Learning models and applications against adversarial threats
Adversarial Robustness Toolbox (ART) provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats.
Open source toolkit that can help users better understand the ways that machine learning models predict labels
AI Explainability 360 is an open source toolkit that can help users better understand the ways that machine learning models predict labels using a wide variety of techniques throughout the AI application lifecycle.
Open source toolkit that can help users understand and mitigate bias in machine learning models throughout the AI application lifecycle
AI Fairness 360 is an extensible open source toolkit that can help users understand and mitigate bias in machine learning models throughout the AI application lifecycle.
Amundsen is a data discovery and metadata engine for improving the productivity of data analysts, data scientists and engineers when interacting with data
Amundsen is a data discovery and metadata engine for improving the productivity of data analysts, data scientists and engineers when interacting with data.
Open source high-performance distributed machine learning platform
Angel is a high-performance distributed machine learning platform. It is tuned for performance with big data from Tencent and has a wide range of applicability and stability, demonstrating increasing advantage in handling higher dimension model.
DELTA is a deep learning based end-to-end natural language and speech processing platform
DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3.
The open standard that simplifies sharing and exchanging metadata.
Egeria is the world’s first open source metadata standard. It provides open APIs, event formats, types and integration logic so organizations can share data management and governance across the entire enterprise without reformatting or restricting the data to a single format, platform, or vendor product.
Open source deep learning framework to build cluster cloud services
EDL optimizes the global utilization of the cluster running deep learning job and the waiting time of job submitters. It includes two parts: a Kubernetes controller for the elastic scheduling of distributed deep learning jobs, and a fault-tolerable deep learning framework.
Open source feature store for machine learning
Feast is an open source feature store for machine learning. It was developed as a collaboration between Gojek and Google in 2018. Feast aims to:
– Provide scalable and performant access to feature data for ML models during training or serving.
– Provide a consistent view of features for both training and serving.
– Enable re-use of features through discovery, documentation, and metadata tracking.
– Ensures model performance by tracking, validating, and monitoring features in production.
An open source scalable policy-based cloud-native machine learning model server
ForestFlow is a scalable policy-based cloud-native machine learning model server. ForestFlow strives to strike a balance between the flexibility it offers data scientists and the adoption of standards while reducing friction between Data Science, Engineering and Operations teams.
Open source distributed training framework for TensorFlow, Keras and PyTorch
Horovod, a distributed training framework for TensorFlow, Keras and PyTorch, improves speed, scale and resource allocation in machine learning training activities. Uber uses Horovod for self-driving vehicles, fraud detection, and trip forecasting. It is also being used by Alibaba, Amazon and NVIDIA.
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is your data, a list of fields to use as inputs, and a list of fields to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.
Open source format to represent deep learning models
With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them.
Enables the creation of educational Data Science programs.
OpenDS4All is an open source project built to accelerate the creation of data science curricula at academic institutions.
Open source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata
Marquez is an open source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata. It maintains the provenance of how datasets are consumed and produced, provides global visibility into job runtime and frequency of dataset access, centralization of dataset lifecycle management, and much more.
Open source similarity search engine for massive-scale feature vectors
Milvus is built with heterogeneous computing architecture for the best cost efficiency. Milvus can be used in a wide variety of scenarios to boost AI application development.
Gstreamer plugins supporting ease and efficiency with neural network models and pipelines
NNStreamer is a set of Gstreamer plugins that support ease and efficiency for Gstreamer developers adopting neural network models and neural network developers managing neural network pipelines and their filters.
Open source universal probabilistic programming language
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.
JanusGraph is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster.
Open source microservices and API management platform
SOAJS is an open source microservices and API management platform, SOAJS eliminates the IT plumbing challenges, so you can deploy microservices significantly earlier and faster. IT initiatives such as digital transformation are simplified, accelerated, cost reduced, and risk mitigated. Our fully integrated, world-class API lifecycle management, multi-cloud orchestration, release management, and IT Ops automation capabilities eliminate your IT organization’s modernization pain.
An effort to increase awareness and data literacy in the data ecosystem
DataPractices is a “Manifesto for Data Practices,” comprised of values and principles to illustrate the most effective, modern, and ethical approach to data teamwork.
Open source and modern interface to scale data science and machine learning workflows using Apache Spark™, R, and a rich extension ecosystem
sparklyr is an open-source and modern interface to scale data science and machine learning workflows using Apache Spark™, R, and a rich extension ecosystem. It enables using Apache Spark with ease using R by providing access to core functionality like installing, connecting and managing Spark and using Spark’s MLlib, Spark Structured Streaming and Spark Pipelines from R.
