Microsoft Cognitive Services vs Neptune

Overview

Microsoft Cognitive Services

Stacks52

Followers34

Votes0

Neptune

Stacks16

Followers38

Votes2

Microsoft Cognitive Services vs Neptune: What are the differences?

Developers describe Microsoft Cognitive Services as "*APIs, SDKs, and services available to help developers build intelligent applications *". Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today. On the other hand, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

Microsoft Cognitive Services and Neptune can be primarily classified as "Machine Learning" tools.

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Detailed Comparison

Microsoft Cognitive Services	Neptune
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today.	It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.
Build confidently with the first AI services to achieve human parity in computer vision, speech, and language; Apply AI to more scenarios with the most comprehensive portfolio of domain-specific AI capabilities on the market; Deploy anywhere from the cloud to the edge with containers	Experiment tracking; Experiment versioning; Experiment comparison; Experiment monitoring; Experiment sharing; Notebook versioning
Statistics
Stacks 52	Stacks 16
Followers 34	Followers 38
Votes 0	Votes 2
Pros & Cons
No community feedback yet	Pros 1 Supports both gremlin and openCypher query languages 1 Aws managed services Cons 1 Doesn't have much community support 1 Doesn't have proper clients for different lanuages 1 Doesn't have much support for openCypher clients
Integrations
No integrations available	PyTorch Keras R Language MLflow Matplotlib

What are some alternatives to Microsoft Cognitive Services, Neptune?

TensorFlow

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

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

PyTorch

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.

Keras

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

Kubeflow

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.

TensorFlow.js

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

Polyaxon

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

Streamlit

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

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

H2O

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.