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  5. Amazon Personalize vs Fritz

Amazon Personalize vs Fritz

OverviewComparisonAlternatives

Overview

Fritz
Fritz
Stacks0
Followers4
Votes0
Amazon Personalize
Amazon Personalize
Stacks20
Followers62
Votes0

Amazon Personalize vs Fritz: What are the differences?

Amazon Personalize: Real-time personalization and recommendation. Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications; Fritz: Mobile machine learning made easy. Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

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

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Detailed Comparison

Fritz
Fritz
Amazon Personalize
Amazon Personalize

Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

Image labeling - With the Image Labeling feature, you can identify the contents of an image or each frame of live video. Each prediction returns a set of labels as well as a confidence score for each label. Image Labeling can recognize people, places, and things. The underlying ML model was trained on millions of images and hundreds of labels.; Object detection - With the Object Detection feature, you can identify objects of interest in an image or each frame of live video. Each prediction returns a set of objects, each with a label, bounding box, and confidence score.
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
0
Stacks
20
Followers
4
Followers
62
Votes
0
Votes
0

What are some alternatives to Fritz, Amazon Personalize?

TensorFlow

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

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

PyTorch

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

Keras

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

NanoNets

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

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

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

Polyaxon

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

Streamlit

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

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.

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