Microsoft Cognitive Services vs Tensorflow Lite

Overview

Microsoft Cognitive Services

Stacks52

Followers34

Votes0

Tensorflow Lite

Stacks74

Followers144

Votes1

Microsoft Cognitive Services vs Tensorflow Lite: 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, Tensorflow Lite is detailed as "Deploy machine learning models on mobile and IoT devices". It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Microsoft Cognitive Services and Tensorflow Lite belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Microsoft Cognitive Services are:

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

On the other hand, Tensorflow Lite provides the following key features:

Lightweight solution for mobile and embedded devices
Enables low-latency inference of on-device machine learning models with a small binary size
Fast performance

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Detailed Comparison

Microsoft Cognitive Services	Tensorflow Lite
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 is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
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	Lightweight solution for mobile and embedded devices; Enables low-latency inference of on-device machine learning models with a small binary size; Fast performance
Statistics
Stacks 52	Stacks 74
Followers 34	Followers 144
Votes 0	Votes 1
Pros & Cons
No community feedback yet	Pros 1 .tflite conversion
Integrations
No integrations available	Python Android OS iOS Raspberry Pi

What are some alternatives to Microsoft Cognitive Services, Tensorflow Lite?

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.