The growth of technology has brought many amazing solutions for businesses worldwide. Machine learning is one of the most favorable and distinctive technological advancements that have revised conventional notions of using data. Machine learning helps in using algorithms to enable systems for performing analysis operations on a large volume of data for making data-based recommendations.
Therefore, data-driven decisions with the help of machine learning can be helpful for businesses to stay ahead of the competition. As a result, the demand for AWS machine learning tools has constantly been increasing in recent times. The following discussion provides an outline of the top machine learning tools on AWS that deliver unique support for different machine learning use cases.
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Here’s the List of Top AWS Machine Learning Tools
The first mention among top AWS machine learning tools is obviously Amazon SageMaker. It is basically a completely AWS-managed platform with capabilities for enabling developers and data scientists to facilitate easier creation, training,& deploying machine learning models. Amazon SageMaker takes away the different barriers that can generally restrict developers who want to utilize machine learning. The modules in Amazon SageMaker could work together or as independent units for building, training,& deployment of machine learning models.
Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth is also one of the notable machine learning tools for developing training datasets with higher accuracy quickly. It provides flexible and simple access to private and public human labelers while facilitating in-build workflows and interfaces suited for common labeling requirements. SageMaker Ground Truth is a formidable mention among AWS machine learning tools for reducing the costs of labeling by almost 70%.
The next prominent mention among machine learning tools by AWS is Amazon Comprehend. It utilizes machine learning for discovering insights and patterns in text by delivering desired natural language processing functionalities. The interesting factor about Amazon Comprehend is the lack of any need for machine learning experience. Amazon Comprehend discovers the language of the text, followed by extracting key phrases, people, events, brands, and places.
Amazon Polly is definitely one of the interesting entries in the list of AWS machine learning tools. Amazon Polly works comprehensively for transforming text into actual speech. It can be the perfect tool for creation of conversational applications, thereby creating the opportunity for developing completely innovative speech-based solutions.
Polly utilizes advanced deep learning technologies for synthesizing speech in a way to sound similar to a human voice. The facility of 47 unique lifelike voices across 24 different languages helps in identifying the ideal voice to build speech-based solutions that can work in various countries.
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Amazon Transcribe is also another promising machine learning tool on AWS for addition of speech-to-text functionalities in their applications. The Amazon Transcribe API can help in analysis of audio files on Amazon S3 to render text files of the transcription of the speech. Interestingly, users can input live audio streams and get a real-time stream of transcripts with the help of Amazon Transcribe. Amazon Transcribe is a trusted Automatic Speech Recognition (ASR) service for various general applications such as transcribing customer service calls.
Another significant entry among AWS machine learning tools is Amazon Lex. It provides functionalities for development of conversational interfaces and their integration into applications that use text and voice. Lex combines deep learning capabilities with automatic speech recognition and natural language understanding. As a result, users can create applications with lifelike conversational interfaces and highly immersive user experiences. Amazon Lex harnesses the power of deep learning technologies to deliver support for creating new solutions with conversational interfaces.
Amazon Rekognition undoubtedly serves as one of the top AWS machine learning tools for its functionality of integrating image analysis on cloud-based applications. Rekognition delivers the capabilities for detection of objects, scenes &faces in images alongside a comparison of faces. Users can leverage Amazon Rekognition API for quick addition of comprehensive deep learning-oriented image classification and visual search capabilities in their applications. Most important of all, users don’t have to make any upfront commitments or pay minimum feesfor using Amazon Rekognition.
Amazon Elastic Inference
The final addition in this list of important machine learning tools on AWS is Amazon Elastic Inference. It can enable the association of cost-effective GPU-centric acceleration with Amazon SageMaker and Amazon EC2 instances. As a result, it can enable the reduction of costs for deep learning inference by a margin of almost 75%. Furthermore, Amazon Elastic Inference also provides support for ONNX, TensorFlow, and Apache MXNet models.
On a final note, it is evident that AWS presents a wide variety of machine learning tools. Machine learning has advanced beyond the identity of just a mere subfield in computer science. The wide range of applications of machine learning in notable examples such as Uber and Wal-Mart showcase the potential for adoption of machine learning.
As the use of smart algorithms continues to increase, the need to learn about machine learning also gains impetus. Machine learning careers present promising opportunities with average annual salaries of machine learning engineers at around $142,000. So, start learning about different AWS machine learning tools and build a future-proof career right now!