Name: Amazon Deep Learning AMIs
Rating: 6.6 (14 reviews)
Author: Amazon AWS

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AMIs are Amazon Machine Images, virtual appliance deployed on EC2. The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at scale. Users can launch Amazon EC2 instances pre-installed with deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.

Qlik Sense® is a self-service BI platform for data discovery and visualization. It supports a full range of analytics use cases—data governance, pixel-perfect reporting, and collaboration. Its Associative Engine indexes and connects relationships between data points for creating actionable insights.

Qlik Sense Enterprise on Windows

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Qlik Sense

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Anaconda

Amazon Deep Learning AMIs

Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

Machine Learning

Likelihood to Recommend

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Founded by Jeff Bezos, the Amazon.com website started in 1995 as a place to buy books because of the unique customer experience the Web could offer book lovers. Bezos believed that only the Internet could offer customers the convenience of browsing a selection of millions of book titles in a single sitting. During the first 30 days of business, Amazon fulfilled orders for customers in 50 states and 45 countries - all shipped from his Seattle-area garage.

Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world's brightest technology minds come to Amazon.com to research and develop technology that improves the lives of shoppers and sellers around the world.

Atlassian Confluence

Atlassian Jira

Asana

Slack

GitHub

Microsoft Visual Studio Code

Weights & Biases

Amazon Deep Learning AMIs 2022-07-19 08:33:38

Amazon Deep Learning AMIs 2021-01-18 04:51:00

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

User interface

Quite confusing

Bit confusing

Novice users

Keep track

Deep learning

Saves time

Pieces of software

Fast and efficient

Home

Machine Learning Tools

We use AWS EC2 for training machine learning models. For spinning up these instances, we use prebuilt AMIs specific for Deep Learning. These AMIs help a lot in setting up the environment very easily without any worries of installing CUDA, Nvidia Drivers, and then specific libraries like PyTorch, and Tensorflow. Support for various Conda environments is also available. The amazing part of this is that it provides support for different types of machines like Ubuntu, Linux, Amazon OS, etc.

Setting up environment
Support for different types of machines
Perfect for Machine Learning / Deep Learning use cases
Nvidia / Cuda / Conda support easily

Simpler documentation of different types of AMIs
Clearly listing out different types of machines as I got confused and spinned up an AMI in Amazon Linux machine instead of Ubuntu
Support for latest version of libraries, to avoid manually updating them after launch

Nvidia / Cuda drivers
Conda environment
Support for Ubuntu (multiple versions)

Saves a lot of Infra Costs
Saves a lot of time in handling environment issues
Easy to start a new instance

Asana, Atlassian Confluence, GitHub, Microsoft Visual Studio Code, Slack, Weights & Biases, Pytorch, TensorFlow

I work within a university setting within the school of computing where we use Amazon Deep Learning AMI as a cloud based alternative for deep learning in practical labs. Though we have high end hardware within our institution, not all students have access to this all of the time and as we have 100+ students on a Data Science/ Machine Learning Program this can cause issues with fair accessibility to equipment. This is where AMI comes into play, as we use this service to allow students to run complicated and intensive algorithms on significant data sets. This removes the need for expensive and inaccessible hardware in our lab spaces and gives our students the flexibility needed when learning about the intricacies of Deep Learning.

You can get several common packages including keras, pytorch and tensorflow setup within an environment ready to code on any AWS instance which saves time
Great for virtual applications that helps communicate between various pieces of software
Not need to worry about compatibility or any major aspects of setup e.g. GPU configuration

Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
It can be a bit confusing to switch between frameworks for novice users

It has made our Data Science/ Machine Learning Courses easier to manage/ need less human input therefore allowing us to increase the cohort size for this degree
It has unified a lot of technologies reducing the load on our IT team

IBM Watson Machine Learning, Azure AI (Cortana), Azure Lab Services

Amazon Deep Learning AMIs

Overview

What is Amazon Deep Learning AMIs?