Amazon SageMaker vs Gradient°

Overview

Amazon SageMaker

Stacks293

Followers284

Votes0

Gradient°

Stacks4

Followers16

Votes0

Amazon SageMaker vs Gradient°: What are the differences?

What is Amazon SageMaker? Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

What is Gradient°? Deep learning platform built for developers. Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows.

Amazon SageMaker and Gradient° can be categorized as "Machine Learning as a Service" tools.

Some of the features offered by Amazon SageMaker are:

Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
Train: one-click training, authentic model tuning
Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling

On the other hand, Gradient° provides the following key features:

1-click Jupyter notebooks
a powerful job runner
Python module to run any code on a fully managed GPU cluster in the cloud

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Detailed Comparison

Amazon SageMaker	Gradient°
A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.	Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows.
Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support; Train: one-click training, authentic model tuning; Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling	1-click Jupyter notebooks; a powerful job runner; Python module to run any code on a fully managed GPU cluster in the cloud; Kubernetes cluster orchestration
Statistics
Stacks 293	Stacks 4
Followers 284	Followers 16
Votes 0	Votes 0
Integrations
Amazon EC2 TensorFlow	Node.js Terraform Golang Python Google Cloud Storage Jupyter Amazon S3

What are some alternatives to Amazon SageMaker, Gradient°?

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.

Inferrd

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GraphLab Create

Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

BigML

BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.

Tinker

Is a training API for researchers and developers.

Sportlingo

AI-powered sports analytics and skill assessment API that enables apps and platforms to deliver personalized training, drills, and performance insights.

AI Video Generator

Create AI videos at 60¢ each - 50% cheaper than Veo3, faster than HeyGen. Get 200 free credits, no subscription required. PayPal supported. Start in under 2 minutes.

Azure Machine Learning

Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

Amazon Machine Learning

This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.