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AWS Direct Connect makes it easy to establish a dedicated network connection from your premises to AWS. Using AWS Direct Connect, you can establish private connectivity between AWS and your datacenter, office, or colocation environment, which in many cases can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections. | PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. |
Reduces Your Bandwidth Costs – If you have bandwidth-heavy workloads that you wish to run in AWS, AWS Direct Connect reduces your network costs into and out of AWS in two ways. First, by transferring data to and from AWS directly, you can reduce your bandwidth commitment to your Internet service provider. Second, all data transferred over your dedicated connection is charged at the reduced AWS Direct Connect data transfer rate rather than Internet data transfer rates.;Consistent Network Performance – Network latency over the Internet can vary given that the Internet is constantly changing how data gets from point A to B. With AWS Direct Connect, you choose the data that utilizes the dedicated connection and how that data is routed which can provide a more consistent network experience over Internet-based connections.;Compatible with all AWS Services – AWS Direct Connect is a network service, and works with all AWS services that are accessible over the Internet, such as Amazon Simple Storage Service (Amazon S3), Elastic Compute Cloud (Amazon EC2), and Amazon Virtual Private Cloud (Amazon VPC). | Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process. |
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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.

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

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

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

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.