In present times, Machine Learning (ML) and Artificial Intelligence (AI) are very popular topics. Generally, you can find these terms almost everywhere in the computing world. But have you heard about AWS deep learning? Deep learning is also an emergent topic that is turning many heads in the present business landscape. With the help of machine learning, we are now able to help machines learn from datasets rather than code fed to them. This is probably one of the oldest pursuits of human civilization, i.e., to make machines learn by themselves.
Deep learning takes the game of machine learning to a new level by leveraging the functionalities of AI with ML. For now, let us think of deep learning as a machine learning method. In the following discussion, let us explore Amazon deep learning in detail with insights on its benefits and applications. Furthermore, a reflection on the different AWS deep learning tools will support the discussion.
What is AWS Deep Learning?
Before diving into the discussion on deep learning with Amazon Web Services, let us take note of deep learning basics. Machines have a lot of data at their disposal, and the generation of new data every day presents a lot of untapped potentials. This is where deep learning comes in with the power of both machine learning and artificial intelligence. The simplest way to define Amazon deep learning is through a reflection on its working.
Deep learning involves training artificial intelligence (AI) for predicting certain outputs based on a set of inputs. The methods of supervised and unsupervised learning are ideal for training the AI. For example, an airplane ticket price estimation tool can use deep learning for predicting the price using specific data. The use of a supervised learning method for AI is evident in this case. The inputs such as origin airport, departure date, destination airport, and the airline are specific and act as labelled datasets.
AWS has brought a new angle to deep learning with Amazon Machine Images (AMIs) specifically meant for machine learning. You can use AWS deep learning AMIs for accessing tools and infrastructure needed to improve deep learning in the cloud. The facility of launching Amazon EC2 instances with pre-installed deep learning frameworks and interfaces.
The general applications of deep learning in the AWS landscape refer to training modern and custom AI models. You can also find deep learning frameworks and interfaces such as TensorFlow and Apache MXNet as ideal for experimenting with new algorithms. The best thing here is that you don’t have to pay for deep learning AMIs on AWS.
Significant Benefits of AWS Deep Learning on the Cloud
Cloud computing for AWS deep learning allows the necessary database to get effectively ingested and managed to control the algorithms. Besides, it lets the learning models for measuring efficiently and significantly reduces the GPU processing power cost. By using different distributed networks, deep learning on AWS through cloud enables you to develop, design, and employ various deep learning applications or software quite faster. Some benefits of this are:
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Great Speed
The algorithms of deep learning are created and designed in such a way that they can learn very quickly. The users can easily faster the training of these learning models, using clusters of GPUs and CPUs. With this, the user can carry out the complicated operation on compute-intensive projects. After that, such models can be used to process the massive amount of data and to get better results.
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Better Scalability
The neural networks of deep learning are perfect for taking advantage of different processors. Besides, they efficiently distribute the workloads among various processors quantities and types. The cloud is full of unlimited resources. So, you can deploy resources virtually to deal with deep learning models.
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Great Flexibility
Apache MXNet, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, TensorFlow, and Kera are some of the important deep learning frameworks. All these frameworks can run on the cloud servers. This lets you use the libraries of deep learning well-suited for your project, whether it is for connected devices, mobiles, or webs.
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Applications of AWS Deep Learning in Different Sectors
Till now, the benefits of deep learning with AWS show promising potential. However, a clear impression of applications in real life could support this discussion further. An understanding of the use of deep learning in different sectors can help us understand its capabilities better. It has been proved that deep learning is perfect for different AWS AI Services cases. Some of these are:
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Computer Vision
By modifying the algorithms with labeled images, you can make the neural network to identify the subjects quite accurately than humans. With the help of AWS AI Services, you can add capabilities, for example, image and video analysis, natural language, virtual assistants, etc. in the applications.
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Speech Recognition
Different patterns and accents of speech in humans can make the process of speech recognition quite difficult for the systems. However, deep learning can quickly and accurately recognize the speech. This is the technology that is employed in Amazon Alexa and other different virtual assistants.
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Processing of Natural Language
The deep learning lets the systems understand the daily conversations, which include critical tone and context. The automated systems like bots come with algorithms that can detect emotions and respond to users usefully.
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Recommendation Engines
Talking about the deep learning success, a few years back, a system was developed which can track the activities of users to offer useful recommendations. By analyzing and comparing the activities, deep learning systems can detect new items that can interest a user. Now let’s have a look into some of the major services which come under deep learning.
Examples of Deep Learning in Different Industries
There are various deep learning applications that are widely used in different industries. The industry can range from medical devices to automated driving. Let’s have a look at the most common industries that are making use of deep learning.
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Automated Driving Industry
Most of the automotive researchers are now using deep learning as it helps in detecting objects automatically. For example, it can detect pedestrians, traffic lights, and signs.
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Defense and Aerospace Industry
Deep learning applications can identify the objects from satellite and also detect safe zoned for military troops.
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Medical Research Industry
Cancer doctors or experts are now greatly using deep learning to detect cancer cells. For example, a team of researchers at UCLA develops a microscope with deep learning to detect cancer cells accurately.
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Industrial Automation
It helps in improving worker safety by automatically detecting when people are within an unsafe distance from machines.
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What are the Amazon Deep Learning Services?
AWS provides a wide assortment of deep learning services. These tools help you simplify and improve deep learning processes at the same time. So, it’s time to keep you’re yourself updated with different deep learning services as this industry is now growing. Here are some services that you need to know about.
Let’s understand some primary AWS deep learning services which can help you in different tasks!
1. AWS SageMaker
Talking about AWS SageMaker, it is a useful service that lets the developers build and train machine learning models. The models are generally used for analytical, or you can say predictive application in AWS. You go through the AWS Machine Learning tutorial to know how to use this service.
It offers numerous benefits for businesses, for example, advanced analysis of customer data, security detection, and more. It supports Jupyter notebook, an open-source web application where developers can share live codes. SageMaker comes with libraries, drivers, and packages for deep learning platforms. It can obtain data from AS3- Amazon Simple Storage Service. There is no such limit of data set size.
2. Amazon Transcribe
One of the best ASR-Automatic Speech Recognition services is Amazon Transcribe. It makes it quite easy for the developers to integrate the speech-to-text feature in the applications. With this, you can quickly analyze audio files and can get a text file of the speech. The service is used for different applications. For example, to transcript the customer service calls, to create subtitles for a video or audio file and more. It can detect MP3 and WAV format audio files. With time the service is continuously learning and enhancing its features to support more types of languages.
3. Apache MXNet on AWS
Apache MXNet is an inference and training framework that comes with an API for AWS deep learning. It has the Gluon interface. The interface lets the developers (both the beginners and professionals) enjoy deep learning on mobile apps and cloud. By using some Gluon code, the developers can create convolutional networks, linear regression, and LSTMs to detect objects, and speeches.
Using SageMaker, you can learn about MxNet. Besides, you can go for the AWS Machine learning tutorial. You can use the AMIs to create a custom environment with MxNet, PyTorch, Caffe 2, Chainer, Microsoft Cognitive Toolkit, and more.
4. Amazon Rekognition
This service makes it quite easy to integrate videos and images into the applications. The process is quite easy. Provide the videos and pictures to the Rekognition. The service will then detect the objects, scenes, people, and activities. The best thing about this is it can identify the inappropriate content. Besides, it offers accurate facial analysis and recognition of videos and images provided by the users. The users can detect and compare different faces for user verifications and counting.
The service is based on deep learning technologies develop by scientists from Amazon computer vision to analyze photos and videos. It doesn’t require any machine learning expertise and can work independently. It is straightforward to use and quickly analyze content saved in Amazon S3. Using this, you can conduct real-time content analysis from the Kinesis Video stream and can analyze images located in S3. If you need to carry out a large project, then it also supports batch analysis.
Understanding the AWS Deep Learning Pricing
If you are worried about AWS deep learning pricing, AWS deep learning cost generally based on the usage of individual service. Your deep leaning monthly bill depends on the combined usage of the services.
You will only pay for what you are using. There is no minimum price of learning. Amazon ML – Amazon Machine Learning generally charges per hour. It considers the compute time invested in evaluating data statistics and models. After that, you need to pay based on the prediction created for the application.
For example, you use around 20 hours of computing time. In the end, you create your models and obtain the batch prediction of 890,000. Here you need to pay the monthly prediction fees and compute fees. The monthly prediction fee is $0.10 for 1000 predictions. For 890,000 prediction, it will be $89. On the other side, the cost of computing is $0.42/hour. So, for 20 hours, you need to pay $ 8.40.
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Final Words
On a concluding note to this discussion, AWS deep learning is something that brings the power of artificial intelligence and machine learning together. AWS offers tools and functions to manage large data sets. Besides, it also provides specialized toolboxes that can help you while working with neural networks, machine learning, computer vision, and more. So, get started with AWS deep learning quickly.
AWS offers a number of certifications to validate the skills of AWS aspirants in various domains. Whether you are a newbie or have gained significant experience, you can choose a certification and validate your skills. For Machine Learning, you can consider taking the AWS Certified Machine Learning exam. We recommend you to take our AWS Certified Machine Learning Specialty practice tests to prepare for the exam and get ahead in your career.
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