Machine Learning on AWS with real life examples
AWS machine learning services provide us different ways to do heavy computation tasks easily. It is a platform that offers flexible, reliable, scalable, easy-to-use, and cost-effective cloud computing solutions.
AWS is an all-inclusive, user-friendly computing platform that Amazon offers. The amalgam of Infrastructure evolves this platform as a Service, packaged Software as a Service, and platform as a Service Offering.
Let’s see the services one by one:
1) Amazon EC2 Instances:
The Amazon Web Services provided the EC2 P3dn instances made for HeavyCompute Tasks (HCT) like machine learning. All these instances are erected with a V100 Tensor Core graphics processing unit and 10 to 100 Gigabits per second of networking throughput. These instances accelerate machine learning training time from days to minutes.
We came across this service when we trained our face identification system with thousands of training images. Using the p3.2xlarge instance, we trained our custom deep learning model in a few hours.
2) Amazon Elastic Inference:
This service allows you to attach GPU-powered acceleration to any normal EC2 and SageMaker instances to reduce the cost of training deep learning models.
Some times we observe GPU instance compute capacity not utilized fully, which leads to cost and waste. But using this service, we can efficiently allocate the Graphics Processing Unit power model that indicates we can adjust the power of GPU according to model usage. In that way, we can manage the extra wastage cost-efficiently.
With this Inference, now you can pick the appropriate instance type that suits the overall central processing unit and memory needs of your app. After that, you can separately configure the required amount of inference acceleration that you efficiently need to use resources and to decrease the cost of running inference.
Example: One of the clients came to us looking for a custom solution which used real-time pattern recognition. We have implemented the deep learning algorithms, and the system used reinforcement learning so as new data comes on the platform the algorithms are trained again based on the new features extracted.
For computing the new features, the client was looking for optimal and scalable options which are both cost-effective and the best. The AWS elastic-inference provided us the capability to ensure that the client’s needs are met.
3) Amazon Mechanical Turk
This service provides the scalable, human workforce to complete jobs like consider a situation where the computers did not work better as compared to humans.
Amazon Mechanical Turk allows companies to manage the collective intelligence, prowess, and insights from a global workforce to modernize traditional business processes, accelerate machine learning development, and augment data collection and analysis.
Let’s check some key benefits of MTurk:
- Optimize efficiency
- Reduce cost
- Increase flexibility
Michael Schmitz, Director of Engineering, Allen Institute for AI says, “At AI2, we’re pushing the state of the art of Artificial Intelligence, which often requires human-annotated data to train new systems and measure our progress.
In particular, we use crowdsourcing platforms such as Amazon Mechanical Turk to build datasets that help our models learn common sense knowledge, which is often necessary to answer basic questions that are easy for humans but still quite hard for machines.
Amazon Mechanical Turk provides a flexible platform that enables us to harness human knowledge to advance machine learning research.”
4) Amazon Rekognition
This service is used for image and video analysis for your applications. It can detect the objects, concepts, activities, people in images or videos.
This service is divided into two parts.
- Amazon Rekognition Image:
- Amazon Rekognition Video:
Amazon Rekognition makes processes easy to add image and video analysis in your apps. You just need to provide an image or a video to the Amazon Rekognition Application Programming Interface, and the service can recognize objects, people, scenes, text, and activities. Moreover, It can remove any inappropriate content, and it provides intelligent facial analysis and facial recognition service. You can effortlessly detect, inspect, and compare faces through user verification, people counting, cataloging, and public safety.
5) Amazon Textract
This service helps to extract the text data from scanned documents or images. Amazon Textract is an optical character recognition that can also identify the data informs and the information stored in tables. With this technology, you can swiftly automate document workflows, allowing you to process countless document pages in hours.
Let’s check the core benefits of Amazon Textract:
- Extract data quickly & accurately
- Lower document processing costs
- No code or templates to maintain
6) Amazon Comprehend
Amazon Comprehend is used for natural language processing (NLP) purposes. Without the machine learning experience, you can use it in machine learning to find out insights and relationships in a text.
7) Amazon Translate
This service used to translate the text into different languages. Amazon Translate provides a neural machine translation service that offers high-quality, fast, and affordable language translation. It’s better than the traditional processes translation it follows deplaning models to provide a more natural-sounding and more accurate translation. Amazon Translate lets you localize content, such as websites and applications for international users.
Example: One of the client came to looking to build a solution which enables the end-user to get whatever is spoken is transcribed into text in real-time, and they were looking for real-time translation into different languages.
8) Amazon Lex
This service provides you the interface to build interactive platforms like chatbots. Amazon Lex provides a fully manageable service where you don’t need to think about managing the infrastructure. With this technology, you can pay for what you use. They’re upfront commitments or minimum fees.
Example: One of the Client came to looking to build a solution which enables the end-user to get whatever is spoken is transcribed into text in real-time. We utilize the power of Amazon lexes, which provided an accurate result. The client was happy with the result.
9) Amazon Forecast
This service provides the forecast on the time series data. Amazon Forecast is a totally managed service that uses ML to deliver highly accurate forecasts. With this technology, you can pay for what you use. They’re upfront commitments or minimum fees.
Example: For one of our client, we implemented linear regressions for forecasting the product requirement based on their historical sales data, which ensured they were never out of stock for specific products during special sessions.
10) Amazon Personalize
Amazon Personalize provides custom recommendations based on your requirements. This service makes development processes easy for developers. Now developers can generate individualized recommendations for customers to operating their applications.
Example: One of the clients came to us looking for a custom solution which used real-time pattern recognition for providing the best alternative for the products on their platform.
11) Amazon SageMaker
Amazon SageMaker service is used for building the machine learning of your own. It provides data scientists and every developer with the capacity to build, train, and deploy ML models quickly. It is a fully-managed service that manages the complete machine learning work processes to label and prepares your data, choose an algorithm, train the model, tune and optimize it for deployment, and take action. With this technology, you can cost-effectively launch your product on time.
If you would like to apply Amazon Web Services (AWS) solutions to your product connect with our expert.
If you would like to apply Machine Learning solutions to your product connect with our expert.