ML Kit vs Amazon Personalize

Overview

ML Kit

Stacks137

Followers209

Votes0

Amazon Personalize

Stacks20

Followers62

Votes0

Amazon Personalize vs ML Kit: What are the differences?

Developers describe Amazon Personalize as "Real-time personalization and recommendation". Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications. On the other hand, ML Kit is detailed as "Machine learning for mobile developers (by Google)". ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

Amazon Personalize and ML Kit are primarily classified as "Machine Learning as a Service" and "Machine Learning" tools respectively.

Some of the features offered by Amazon Personalize are:

Combine customer and contextual data to generate high-quality recommendations
Automated machine learning
Continuous learning to improve performance

On the other hand, ML Kit provides the following key features:

Image labeling - Identify objects, locations, activities, animal species, products, and more
Text recognition (OCR) - Recognize and extract text from images
Face detection - Detect faces and facial landmarks

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Detailed Comparison

ML Kit	Amazon Personalize
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.	Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.
Image labeling - Identify objects, locations, activities, animal species, products, and more; Text recognition (OCR) - Recognize and extract text from images; Face detection - Detect faces and facial landmarks; Barcode scanning - Scan and process barcodes; Landmark detection - Identify popular landmarks in an image; Smart reply - Provide suggested text snippet that fits context	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;
Statistics
Stacks 137	Stacks 20
Followers 209	Followers 62
Votes 0	Votes 0

What are some alternatives to ML Kit, Amazon Personalize?

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/

NanoNets

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.

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.

Inferrd

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.