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Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications. | It is an open-source, Kubernetes-native workflow orchestrator implemented in Go. It enables highly concurrent, scalable, and reproducible workflows for data processing, machine learning and analytics. |
Combine customer and contextual data to generate high-quality recommendations; Automated machine learning; Continuous learning to improve performance; Bring your own algorithms; Easily integrate with your existing tools; | Write locally, execute remotely;
Scale as fast as your imagination;
Give the power back to data practitioners and scientists;
Create extremely flexible data and ML workflows |
Statistics | |
GitHub Stars - | GitHub Stars 6.6K |
GitHub Forks - | GitHub Forks 757 |
Stacks 20 | Stacks 2 |
Followers 62 | Followers 2 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.