--- /dev/null
+# OSS Projects related to AI/ML for NFV Usecases\r
+\r
+| Project | Description | Analytical Use Cases |\r
+| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------- |\r
+| PNDA | Open source Platform for Network Data Analytics.<br>Aggregates data like logs, metrics and network telemetry.<br>Efficiently distributes data with publish and subscribe model.<br>Manages lifecycle of applications that process and analyse data. \[11\] | Predictive analytics..<br>Path Anomaly detection using in-band OAM.<br>Service Assurance. |\r
+| Snas | Streaming Network Analytics System is a framework to collect, track and access tens of millions of routing objects in real time. \[13\] | Predictive analysis. |\r
+| DCAE/Holmes<br>(ONAP) | DCAE is the umbrella name for a number of components collectively fulfilling the role of Data Collection, Analytics, and Events generation for ONAP.<br>Holmes project provides alarm correlation and analysis for cloud infrastructure and services, including hosts, vims, VNFs and NSs. | Event Correlation<br>Root Cause analysis |\r
+| Vitrage<br>(Openstack) | Vitrage is the OpenStack RCA (Root Cause Analysis) service for organizing, analysing and expanding OpenStack alarms & events, yielding insights regarding the root cause of problems and deducing their existence before they are directly detected. \[15\] | Root Cause Analysis. |\r
+| Acumos AI | Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation | Traffic Prediction, |\r
+| EDL is an Elastic Deep Learning | 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. | Fault Tolerance |\r
+| AI Explainability 360 | 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. | Predictive analysis. |\r
+| ONNX: Open Neural Network Exchange | With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. | [Machine Translation, Image Classification](https://github.com/onnx/models#machine_translation) |\r
+| Pyro | 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 | Deep Generative Models,Time Series (Pytorch) |\r
+| Horovod | 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. | Fraud Detection, Trip Forecasting |\r
+| Ludwig | 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. | |\r
+| Angel ML | 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. | Angel offers several deployment options such as Docker, Yarn and Kubernetes, |\r
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