Overview of all AI based Amazon Web Services (AWS)
AI is being used all across Amazon. On amazon.com, users see recommendations suggested by Amazon’s recommendation engine,which improves their shopping experience. AI is used to spot trends in the customer’s experience so that new products can be developed andexisting products enhanced. In the fulfillment and logistic departments, robots pick, pile,sort, and move boxes around so that they can be shipped to customers. Amazon employees used to have to walk miles each day. By using AI, they save time and free up staff to serve more customers faster.
Now AWS has made AI tools broadly available so that businesses can innovate and improve their products. Amazon Web Services offers a range of services in AI by leveraging Amazon’s internal experience with AI and machine learning. These services are separated here according to four layers,
- AI services
- AI platforms
- AI frameworks
- AI Infrastructure.
They organize from the least complex to the most complex going from top to bottom. Let’s take a brief look into each of these layers.
AI services are each built to handle specific common AI tasks. These services enable developers to add Intelligence to their applications through an API called to pre-train services rather than developing and training their own deep learning models.
Amazon Recognition makes it easy to add image analysis for your applications. With rekognition, you can detect specific objects, scenes, and faces like celebrities and identify inappropriate content in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
Amazon Polly is a service that turns texts into lifelike speech, allowing you to create applications that talk and build entirely new categories of speech enabled product. Amazon Polly’s text-to-speech service uses advanced deep learning technologies to synthesize speech that sounds like human voice.
Amazon Lex is a service for building conversational interfaces into any application using voice and text. It provides automatic speech recognition for converting speech-to-text and natural language understanding to recognize the intent of the text. That lets you build applications with highly engaging user experiences and life-like conversational interactions.
The AI platforms layer of the stack includes products and frameworks that are designed to support custom AI related tasks, such as training a Machine Learning model with your own data.
For customers who want to fully manage platform for building models using their own data, we have Amazon Machine Learning.
https://aws.amazon.com/machine-learning/
It’s designed for developers and data scientists who want to focus on building models. The Platform removes the undifferentiated overhead associated with deploying and managing infrastructure for training and hosting models. It can analyze your data, provide you with suggested transformations for the data, train your model, and even help you with evaluating your model for accuracy.
Amazon EMR is a flexible, customizable, and manage big data processing platform. It’s a manage solution in that it can handle things like scaling and high availability for you. Amazon EMR does not require a deep understanding of how to set up and administer Big Data Platforms, you get a preconfigured cluster ready to receive your analytics workload. It is built for any Data Science Workload not just AI.
Apache Spark is an open-source, distributed processing system commonly used for Big Data workloads. Apache Spark utilizes in-memory caching and optimize execution for fast performance and it supports general batch processing, Streaming Analytics, Machine Learning, graph database, and ad hoc queries. It can be run and managed on Amazon EMR clusters.
The AI frameworks and infrastructure layers are for expert machine learning practitioners. In other words, for the people who are comfortable building deep learning models, training them, doing predictions, also known as inference, and getting the data from the models into production applications.
The underlying infrastructure consists of Amazon EC2 P3 instances, which are optimized for machine learning and deep learning. Amazon EC2 P3 instances provide powerful NVIDIA GPUs to accelerate computations, so that customers can train their models in a fraction of the time required by traditional CPUs.
After training, Amazon EC2 C5 compute optimize and aim for general-purpose instances. In addition to GPU based instances, are well-suited for running inferences with the training model.
AWS supports all the major deep-learning frameworks and makes them easy to deploy without AWS, deep-learning Amazon machine image which is available for Amazon Linux and Ubuntu, so that you can create managed, automatically scalable clusters of GPUs for training and inference at any scale. It comes pre-installed with technologies like Apache MX net, tenser flow, Cafe and Caffe2 and auto-populate Machine Learning software such as the Anaconda package for data science.
Now let’s go through a few use cases.Almost all industry domains are now innovating with AWS AI. For example,
- Fraud.net uses Amazon Machine Learning to support its Machine Learning models.
- The company uses Amazon DynamoDB and AWS Lambda to run code without provisioning and managing servers.
- Fraud.net also uses Amazon Redshift for data analysis.
What are the benefits that they get from that setup?
Fraud.net launches and trains Machine Learning models in almost half the time it took on other platforms. It reduces complexity and makes sense of emerging Fraud patterns. It saves customers about a million dollars each week.
To summarize, you can create an impact in your business by automating repetitive and manual tasks, engaging customers and optimizing product quality using AI.
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Hi all! Hope this was helpful!
By Varishu Pant
-Statistician, Data Scientist and Lyricist.
For any suggestions, corrections or just to have a chat with me, reach me here-
https://www.linkedin.com/in/varishu-pant/
Also, check out my other blogs (like Unobserved Components Model) published in Analytics Vidhya here-
https://medium.com/@varishupant
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