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Introduction to Machine Learning with AWS - Part 3
- Adit Modi
Machine learning (ML) has become a core technology ingredient in a wide range of use cases from natural language processing and computer vision to fraud detection, demand forecasting, product recommendations, preventive maintenance, and document processing.
Harnessing the benefits of machine learning at scale requires standardizing on a modern ML development process across your business.
In this blog post, we will discuss some of the most important AWS machine learning services that helps customers Modernize their ML development process which can accelerate their pace of innovation by providing scalable infrastructure, integrated tooling, healthy practices for responsible use of ML, a choice of tools accessible to developers and data scientists of all ML skill levels, and efficient resource management to keep costs low.
The Introduction to AWS is a Series containing different articles that provide a basic introduction to different aws topics/categories. Each article covers the detailed guide on how to work with particular topic/category . This series aims at providing "A Getting Started Guide on Different aws topics / categories ."
AWS Machine Learning Services
- AWS infuses intelligence into your contact center and reduce costs with automated ML.
- AWS helps customers apply ML to videos, webpages, APIs, and more to enhance discovery, localization, compliance, and monetization.
- AWS helps accelerate machine learning innovation at scale while reducing costs.
Apache MXNet on AWS
Apache MXNet on AWS is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning.
MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps.
In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization.
You can get started with MxNet on AWS with a fully-managed experience using SageMaker, a platform to build, train, and deploy machine learning models at scale.
Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit.
AWS Deep Learning AMIs
- The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale.
- You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.
- AWS DeepComposer is the world’s first musical keyboard powered by machine learning to enable developers of all skill levels to learn Generative AI while creating original music outputs.
- DeepComposer consists of a USB keyboard that connects to the developer’s computer, and the DeepComposer service, accessed through the AWS Management Console. DeepComposer includes tutorials, sample code, and training data that can be used to start building generative models.
- AWS DeepLens helps put deep learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.
AWS DeepRacer is a 1/18th scale race car which gives you an interesting and fun way to get started with reinforcement learning (RL). RL is an advanced machine learning (ML) technique which takes a very different approach to training models than other machine learning methods.
Its super power is that it learns very complex behaviors without requiring any labeled training data, and can make short term decisions while optimizing for a longer term goal.
With AWS DeepRacer, you now have a way to get hands-on with RL, experiment, and learn through autonomous driving. You can get started with the virtual car and tracks in the cloud-based 3D racing simulator, and for a real-world experience, you can deploy your trained models onto AWS DeepRacer and race your friends, or take part in the global AWS DeepRacer League. Developers, the race is on.
TensorFlow on AWS
TensorFlow enables developers to quickly and easily get started with deep learning in the cloud. The framework has broad support in the industry and has become a popular choice for deep learning research and application development, particularly in areas such as computer vision, natural language understanding and speech translation.
You can get started on AWS with a fully-managed TensorFlow experience with SageMaker, a platform to build, train, and deploy machine learning models at scale.
Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with TensorFlow and other popular frameworks including Apache MXNet, PyTorch, Caffe, Caffe2, Chainer, Gluon, Keras, and Microsoft Cognitive Toolkit.
AWS Inferentia is a machine learning inference chip designed to deliver high performance at low cost. AWS Inferentia will support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as well as models that use the ONNX format.
Making predictions using a trained machine learning model–a process called inference–can drive as much as 90% of the compute costs of the application. Using Amazon Elastic Inference, developers can reduce inference costs by up to 75% by attaching GPU-powered inference acceleration to Amazon EC2 and SageMaker instances.
However, some inference workloads require an entire GPU or have extremely low latency requirements. Solving this challenge at low cost requires a dedicated inference chip.
AWS Inferentia provides high throughput, low latency inference performance at an extremely low cost. Each chip provides hundreds of TOPS (tera operations per second) of inference throughput to allow complex models to make fast predictions.
For even more performance, multiple AWS Inferentia chips can be used together to drive thousands of TOPS of throughput. AWS Inferentia will be available for use with SageMaker, Amazon EC2, and Amazon Elastic Inference.
As AWS releases more and more services covering ML, they are helping to bridge the gap between having the traditional skill set of a ML engineer to those looking to venture into the ML arena for the first time. This is allowing people to become skilled with using ML technology without having to be an expert in the traditional skillset.
Hope this guide helps you with the Introduction to Machine Learning with AWS - Part-3.
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