Amazon Machine Learning vs Cloud AI Platform Pipelines

Overview

Amazon Machine Learning

Stacks165

Followers246

Votes0

Cloud AI Platform Pipelines

Stacks0

Followers6

Votes0

Amazon Machine Learning vs Cloud AI Platform Pipelines: What are the differences?

Developers describe Amazon Machine Learning as "Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology". 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. On the other hand, Cloud AI Platform Pipelines is detailed as "Deploy robust, repeatable machine learning pipelines". It provides a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility, and delivers an enterprise-ready, easy to install, secure execution environment for your ML workflows.

Amazon Machine Learning and Cloud AI Platform Pipelines belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by Amazon Machine Learning are:

Easily Create Machine Learning Models
From Models to Predictions in Seconds
Scalable, High Performance Prediction Generation Service

On the other hand, Cloud AI Platform Pipelines provides the following key features:

Push-button installation via the Google Cloud Console
Enterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and more
Seamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many others

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Detailed Comparison

Amazon Machine Learning	Cloud AI Platform Pipelines
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.	It provides a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility, and delivers an enterprise-ready, easy to install, secure execution environment for your ML workflows.
Easily Create Machine Learning Models;From Models to Predictions in Seconds;Scalable, High Performance Prediction Generation Service;Low Cost and Efficient	Push-button installation via the Google Cloud Console; Enterprise features for running ML workloads, including pipeline versioning, automatic metadata tracking of artifacts and executions, Cloud Logging, visualization tools, and more; Seamless integration with Google Cloud managed services like BigQuery, Dataflow, AI Platform Training and Serving, Cloud Functions, and many others ; Many prebuilt pipeline components (pipeline steps) for ML workflows, with easy construction of your own custom components
Statistics
Stacks 165	Stacks 0
Followers 246	Followers 6
Votes 0	Votes 0
Integrations
No integrations available	Google BigQuery Google Cloud Functions Google Cloud Dataflow Google Kubernetes Engine Google AI Platform

What are some alternatives to Amazon Machine Learning, Cloud AI Platform Pipelines?

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

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.

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.

Amazon SageMaker

A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

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.