Looking for some of the best machine learning tools on AWS? Here your search ends! Let’s get familiar with some basic details and dive deep into the list of top AWS machine learning tools.
Machine learning may be a new term for many, although having been popular across different sectors. Back in 1952, Arthur Samuel first coined the term “Machine Learning,” thus establishing the foundation for one of the radical technological interventions. In present times, machine learning technology is a vital tool for obtaining predictions and valuable insights regarding business operations.
AWS has been one of the frontrunners in the field of machine learning alongside its other counterparts. However, the effectiveness of AWS machine learning tools is one of the foremost highlights that provide a competitive advantage to AWS. The following discussion aims to reflect on some of the notable machine learning tools of AWS. This knowledge will also help you in your preparation for the AWS Machine Learning certification. Readers can find out more about the efficiency of AWS as a reliable platform for machine learning from this discussion.
Importance of Machine Learning Tools of AWS
Amazon Web Services is the leading public cloud service provider and has a wide array of cloud services and technologies on offer. Therefore, you could also find AWS machine learning tools suited to your various enterprise requirements. AWS provides a wider and deeper variety of machine learning and AI services for different businesses.
The machine learning tools on AWS primarily aimed at helping customers in addressing critical challenges that restrict developers from leveraging the optimal power of machine learning. Users could select pre-trained AI services to address applications of forecasting, computer vision, recommendations, and language processing.
On the other hand, AWS also provides tools for faster creation, training, and deployment of machine learning models with higher scalability. Users also have the advantage of building custom models while ensuring compatibility with major open-source frameworks.
The most promising strength of AWS machine learning tools is that they are based on a highly comprehensive cloud platform. AWS is ideally optimized for machine learning with the facility of high-performance compute and a lack of compromises in security and analytics. All of these aspects clearly establish the necessity for finding out the machine learning tools offered by AWS.
New in Machine Learning? Read our previous blog to understand the basics of Amazon Machine Learning.
List of Top AWS Machine Learning Tools
Now when you have understood the importance of AWS machine learning tools, it’s time to check out the top AWS machine learning tools. These machine learning solutions help in building and deployment of the machine learning models. Let’s move to the list:
Amazon SageMaker is always the obvious addition among machine learning solutions in the AWS marketplace. It is a fully-managed platform that helps data scientists and developers ensure the easier and faster building, training, and deployment of machine learning models at a different scale. Amazon SageMaker clips off all the barriers which generally slow down developers aspiring to use machine learning.
Machine learning generally presents difficulty in learning due to the complex processes for building and training the models. In addition, the deployment of machine learning models into production is also slow and complicated. Furthermore, the expertise required for all these processes alongside other resource requirements presents many barriers to machine learning for developers.
Amazon SageMaker removes the complexity and helps developers understand and utilize the full potential of all steps in machine learning. The modular design of Amazon SageMaker makes it one of the most flexible machine learning tools on AWS. You can use the different modules together or independently for building, training, and deploying machine learning models.
Datasets are the lifeblood of machine learning, and Amazon SageMaker Ground Truth offers the platform for the development of training datasets for machine learning with higher accuracy and speed. SageMaker Ground Truth is one of the top AWS machine learning tools because it provides easy access to public and private human labelers. In addition, it also facilitates labelers with interfaces and in-built workflows for general labeling tasks.
Most important of all, SageMaker Ground Truth can reduce labeling costs by almost 70% through automatic labeling. The effective use of machine learning for automatic data labeling offers better cost savings and productivity. The SageMaker Ground Truth model gradually becomes efficient over time through learning continuously from labels by human labelers. As a result, it can improve its capability for labeling more data automatically and contributing to faster training of datasets.
Also Read: Deep Learning on AWS
The next promising addition among Amazon machine learning tools is Amazon Lex. It is a service for developing conversational interfaces in any application through the use of voice and text. Lex offers the functionalities of advanced deep learning in the form of automatic speech recognition (ASR) for the conversion of speech to text. In addition, it also provides natural language understanding features for recognizing the intent in a text.
As a result, it can enable the development of applications with highly interactive user experiences and almost real conversational interactions. Amazon Lex simplifies access to speech recognition and natural language understanding alongside presenting the power of Alexa to all developers. It is one of the leading technologies for the development of entirely new categories of products created only through conversational interfaces.
One of the striking AWS machine learning tools is AWS Inferentia. It is a machine learning inference chip that aims at delivering higher performance at lower costs. AWS Inferentia offers support for Apache MXNet, PyTorch, and TensorFlow deep learning frameworks and models using the ONNX format. AWS Inferentia facilitates higher throughput, low latency inference performance at unbelievably low costs.
Every chip can assure hundreds of TOPS (Tera Operations Per Second) of inference throughput for allowing complex models to ensure faster predictions. Users can also use AWS Inferentia chips in combination to achieve additional TOPS of throughput. In addition, it will be supported on Amazon Elastic Inference, Amazon SageMaker, and Amazon EC2.
Amazon Textract is undoubtedly one of the productive Amazon machine learning tools. It is a service that extracts text and data automatically from scanned documents. Amazon Textract offers more than the capabilities of optical character recognition (OCR) and helps in the identification of content in the fields through forms and information stored in tables.
Textract addresses the challenges of slow and expensive manual data entry processes for the extraction of data from documents. It also enables faster automation of document workflows, thereby ensuring that you can process many documents within hours. After capturing the information, you can take necessary action on it. Users can also create automated approval workflows and smart search indexes with Textract. Furthermore, it also offers better compliance with rules of document archival.
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Amazon Comprehend is the foremost entry among AWS machine learning tools that comes to mind when you think of Natural Language Processing (NLP). It is an NLP service based on machine learning for finding insights and relationships between various attributes in text. Amazon Comprehend utilizes machine learning for discovering new insights and relationships in the available unstructured data.
It can identify the language in the text and extract key phrases, events, places, brands, and people in a text. Amazon Comprehend utilizes tokenization and parts of speech for analysis of text and automatic organization of a set of text files according to the topic. The AutoML features in Amazon Comprehend can also help in creating a custom set of text classification models or entities built specifically according to an enterprise’s needs.
Amazon Rekognition is among the many common AWS machine learning tools that you can find at present. It is a service that helps in adding image analysis capabilities to different applications. Rekognition can help in the detection of objects, faces, and scenes in particular images. It can also help in searching and comparing faces.
The Amazon Rekognition API provides the ease of adding advanced deep-learning-based visual search and image classification capabilities to applications. Amazon Rekognition leverages deep neural network models for the detection and labeling of multiple objects and scenes in images. As a result, you can find Amazon Rekognition as a vital tool for integrating powerful visual search and discovery functionalities into an application.
Amazon Elastic Inference is also one of the formidable entries among AWS machine learning tools. It helps in attachment of low-cost GPU-based acceleration with Amazon SageMaker and EC2 instances for reduction of the costs in running deep learning inference by almost 75%.
Amazon Elastic Inference addresses the problems of resource inefficiency in GPU compute by attacking the right amount of GPU-based inference acceleration to EC2 or SageMaker instance types without modifications in the code. Users can select the instance type suited perfectly for the overall CPU and memory requirements of an application. You can also configure the amount of inference acceleration for efficient use of resources and reduction in costs of running inference.
Amazon Translate is one of the productive AWS machine learning tools with the maximum potential of machine learning for users. It is a neural machine translation device for faster, affordable, and highly accurate language translation. Amazon Translate helps in localization of content such as applications and websites for international users. Its primary functionalities are evident in the easier translation of large volumes of text with the assurance of efficiency.
Preparing for a Machine Learning interview? Here are the top Machine Learning interview questions that will get you ready for the interview.
Bottom Line
On a concluding note, it is inevitable to note that covering all AWS machine learning tools in a limited discussion is quite hard. There are many other notable machine learning tools such as Amazon Forecast, Amazon DeepRacer, Amazon Personalize, Amazon DeepLens, Amazon Transcribe, TensorFlow on AWS, and others. All of the tools have specific functionalities that simplify the work of developers and data scientists.
Machine learning tools offer sophisticated frameworks for data analysis alongside reliable tools for developers to add application functionalities. Interestingly, Amazon continues to add new machine learning tools and services frequently alongside introducing new features in existing solutions.
Learn more about the machine learning tools of AWS and validate your expertise with the AWS Machine Learning Specialty certification exam. Enroll in our AWS Machine Learning Specialty training courses and try out the practice tests to give your preparation a new edge.
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AWS Transcribe Video and Audio Transcription Process
Around the world, businesses need a fast and reliable method to transcribe a video or audio file usually in multiple languages. The audio and video content can vary from a product demonstration, job interviews, news broadcasts, or call center phone interactions. The traditional manual method used for transcription is not only expensive but lengthy too. But now it has become easier with Amazon’s Transcribe service.