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Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn complex ML algorithms & technology

What is 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.
Amazon Machine Learning is a tool in the Machine Learning as a Service category of a tech stack.

Who uses Amazon Machine Learning?

8 companies use Amazon Machine Learning in their tech stacks, including Apli, Cymatic Security, and FetchyFox.

9 developers use Amazon Machine Learning.

Why developers like Amazon Machine Learning?

Here’s a list of reasons why companies and developers use Amazon Machine Learning
Top Reasons
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Amazon Machine Learning Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon Machine Learning in their tech stack.

Julien DeFrance
Julien DeFrance
Full Stack Engineering Manager at ValiMail · | 2 upvotes · 5.2K views
Amazon SageMaker
Amazon Machine Learning
AWS Lambda

Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

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Amazon Machine Learning's features

  • Easily Create Machine Learning Models
  • From Models to Predictions in Seconds
  • Scalable, High Performance Prediction Generation Service
  • Low Cost and Efficient

Amazon Machine Learning Alternatives & Comparisons

What are some alternatives to Amazon Machine Learning?
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.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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.
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.
See all alternatives

Amazon Machine Learning's Stats

- No public GitHub repository available -