Amazon Web Service (AWS) is the biggest cloud infrastructure with 175 featured services, managing everything from machine learning and the Internet of Things (IoT) to data analytics. Amazon commands its position as one of the front runners in the machine learning services concepts alongside its counterparts.
Over the last two years, the US tech giant has invested significantly in this technology, making it hassle-free for developers to develop and deploy. Most organizations leave no stone unturned to stay ahead on top of things in the current tech environment.
Machine learning is one of the fastest-growing solutions in technology. Many tech giants have adopted machine learning on cloud technology and aced their growth to stay in this competitive world for a long time.
In its recent report on the cloud, tech firm Flexera revealed that 81% of heavy cloud users have been using AWS ML tools more frequently over a long time. In addition, according to a separate analysis by the World Economic Forum, 97 million new roles may emerge in machine learning services and Artificial Intelligenceby 2025 for developers.
ML is one of the pivotal technologies for many enterprises. Despite the scope of investment and improvement, training, maintaining, and developing MI models has been cumbersome and ad-hoc. AWS machine learning tools are different products that offer multiple patterns like improving customer experience, making accurate predictions, getting deeper insights from data, and reducing operational overhead for developers.
The global cloud machine learning market is projected to achieve a valuation of USD 14.6 billion by 2023, with AWS prominently dominating this space (source: Statista). Holding a substantial market share, AWS SageMaker is a preferred cloud ML platform by companies across diverse industries and scales (source: Gartner).
As indicated by a recent survey conducted by Ventana Research, 71% of AI and ML professionals opt for AWS to power their machine learning projects, underlining the platform’s widespread adoption and influence in the field. These statistics emphasize AWS’s stronghold in the cloud machine learning sector, reflecting its popularity and trust among industry practitioners.
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Overview of Amazon Web Services
AWS, short for Amazon Web Services, stands as Amazon’s cloud service platform, offering organizations the adaptability and scalability needed for deploying services and handling data across various sizes. Rather than investing in physical servers, AWS ML tools enable companies to utilize and pay for specific resources like database storage, computational capabilities, content delivery, and on-demand services such as AWS Machine Learning services. Notable competitors in this domain include Microsoft Azure and Google Cloud. Amazon AI tools empower organizations to leverage an expanding array of services and capabilities without needing in-house development, leading to cost savings and accelerated deployment times.
Factors driving companies to choose AWS ML tools over other cloud services encompass the following:
1) Security
AWS prioritizes security through end-to-end data encryption, using robust protocols like TLS and SSL for transit and offering services like AWS Key Management for secure data storage. This comprehensive approach mitigates the risk of unauthorized access and data breaches, instilling trust and meeting compliance standards.
2) Experience
With its early entry into cloud computing, Amazon tools draws upon years of valuable experience, positioning itself to provide top-notch solutions based on industry best practices.
3) Flexibility
AWS stands out for its remarkable flexibility, allowing developers to choose the operating system, programming language, and database according to their project requirements.
4) Usability
Developers find AWS machine learning services list relatively user-friendly, enabling them to swiftly deploy applications, create new ones, or seamlessly migrate existing ones. The platform’s intuitive interface contributes to an efficient development experience.
5) Scalability
AWS offers scalable solutions, allowing developers to adjust resources based on user requirements. Whether scaling up to handle increased demand or scaling down during quieter periods, top AWS services accommodate the dynamic needs of applications and businesses.
AWS machine learning services list offers various cloud services, technologies, and a more comprehensive and deep variety of MI services for different businesses. Before adopting ML tools, review the detailed info and pick the best service that suits your organization. The American organization is currently offering 20 machine learning tools on its platform. Let’s dive deep into the top 15 of the machine learning tools offered by Amazon Web Services.
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Top 15 of the Machine Learning Tools Offered by Amazon Web Services
1) SageMaker
AWS SageMaker is a cloud-based machine learning services that empowers developers and data scientists to create, train, and deploy ML models into a production-ready hosted environment within a single platform. This ML tool has an auto-pilot option, automatically processing and running the data into multiple algorithms. It also helps developers pick the best algorithm for their solution instead of manually training and testing multiple models.
The tool is apt for data scientist who wants to build end-to-end machine learning as a service for their projects, and it is also a fast, efficient, and cost-effective platform. SageMaker makes it easier to handle MI model concepts from research to production quickly and is more progressive, predictable, and even more advanced.
Amazon unveiled six new AWS SageMaker features, which are:
A) SageMaker Canvas
Using a visual point-and-click interface for business analysts, SageMaker Canvas generates more accurate machine-learning predictions, and no code is required. It aims to help business analysts build machine learning models without depending on data engineers. An analysis by Gartner survey predicted that 70% of new applications developed by enterprises will operate no code (or) low code technologies by 2025.
B) SageMaker Ground Truth Plus
It provides fully managed data labeling operations that quickly build highly accurate training datasets and a highly skilled workforce for machine learning services. Ground Truth Plus is a service of AWS SageMaker that offers data labeling services to customers quickly and reduces prices by up to 40 percent by using an expert workforce.
C) SageMaker Studio
It is a free service (no charge, no-setup notebook) built for learning and experimenting with AWS machine learning tools. However, data scientists, developers, and students prefer the SageMaker Studio service to learn and experiment with ML.
D) SageMaker Training Compiler
It guides training deep learning models up to 50 percent faster through more efficient use of GPU instances. In addition, compilers are entirely responsible for translating programming languages like Python or Java into machine code.
E) SageMaker Inference Recommender
SageMaker Inference Recommender is a new service tool that allows data engineers to safely decrease the needed time to get machine learning models into the production environment. In addition, it automates load testing and model tuning across machine learning as a service instance with the best price performance.
F) SageMaker Serverless Inference
This new tool allows users to deploy machine learning models for ML inference without having any underlying infrastructure. This tool is cost-effective for clients with unpredictable prediction traffic patterns with long idle times.
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2) CodeGuru
Amazon CodeGuru is a new tool for developers that recommends writing high-quality, cost-efficient Java code. It consists of mainly two components – Profiler and Reviewer.
A) CodeGuru Profiler
CodeGuru profiler searches the data runtime performance of your live application. It improves ways to fine-tune your application performance, such as excessive usage of inefficient libraries, expensive deserialization, excessive logging, and expensive objects.
B) CodeGuru Reviewer
AWS CodeGuru Reviewer is a tool that uses machine learning as a service and program analysis to find critical issues, such as bugs and security, which are hard for developers to detect during application development. It also provides suggestions for improving your Python and Java code.
3) Comprehend
Using machine learning and natural language processing (NPL) tools, AWS Comprehend allows you to detect relationships and valuable insights in the text. Amazon Comprehend provides six different APIs (Application Program Interfaces) to gather insights from text.
A) Language Identification API
B) Entity Recognition API
C) Key phrase Extraction API
D) Personally Identifiable API
E) Syntax API
F) Sentiment Analysis API
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4) Forecast
AWS Forecast is a fully managed machine learning service designed to automate the data, detect the key attributes, and pick suitable algorithms to produce an accurate time-series forecast. This technology offers future business outcomes for FBA sellers, including product demands, financial performance, and resource needs, by using ML software.
5) Fraud Detector
Unlike many services on the market, the Amazon fraud detector is a highly specialized AI tool built to quickly identify potentially fraudulent activities such as stolen debit cards, credit cards, and fake registrations. The Amazon Fraud detector provides a unique model to catch fraud faster across various use cases with future transformation, enrichments, and tailored algorithms.
6) Kendra
Powered by machine learning solutions, Amazon Kendra is an intelligent search service that uses natural language to find results accurately for your application and websites based on customer queries. Using Amazon Kendra, users can more simply find the information they require within the bulk of data spread across various sources.
7) Lex
AWS Lex is a fully managed web platform that permits developers to publish text chatbots or voice across multiple platforms like chat services, web apps, and mobile devices. With this new tool, no deep learning expert is essential to create a text chat boot, and you specify the conversion workflow in Amazon Lex.
Under the hood, AWS Lex provides automatic speech recognition (ASR) that helps to convert speech to text. Additionally, it uses natural language understanding (NLU) that helps recognize the text’s intent.
8) Personalize
It is a low code matured machine learning on the cloud service designed for customers to create private and customized personalization recommendations through an application program interface (API). No machine learning solutions expertise is needed.
It makes production much more straightforward for developers to build an application that delivers personalized experiences such as customized direct marketing, product recommendations, and personalized product ranking.
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9) Polly
AWS Polly is an advanced Text-to-Speech service that converts text into human-like text-to-speech voices. Moreover, it offers lifelike voice outputs across Japanese, Korean, and Chinese languages. This allows users to develop automated responses in the languages of their choice and convenience.
10) Rekognition
Amazon Rekognition is a cloud-based service that makes it simple to join your application’s image analysis and video analysis by using deep learning, highly scalable, and proven technology without having the ML tool.
11) Textract
AWS Textract is a deep learning-based service that automatically extracts text and handwriting, detecting data from scanned copies. Before the discovery of Amazon Textract, enterprises followed the traditional way of hiring a person to extract the data from documents such as tax documents or contracts.
12) Transcribe
Amazon Transcribe is an automatic speech-to-text solution platform that uses ML models to convert audio to text and produce a review or read transcripts. AWS introduced a new Amazon Transcribe Call Analytics feature that lets you extract valuable insights from a client conversation with an API call.
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13) Translate
Amazon Translate is an AI/ML family member, and it is a neural machine translation service that allows you to translate a bulk amount of text from one language to another. It supports 75 languages, such as Hindi, Tamil, Telugu, Gujarati, Malayalam, Chinese (Traditional), Spanish, French, Russian, and many more to the list. Additionally, Amazon Translate supports 5000 language combinations.
14) DeepLens
Amazon DeepLens is a machine learning-enabled video camera with built-in deep learning capabilities that help you recognize objects or characters appearing in a real-time video stream using AI technology.
15) DeepRacer
If you love self-driving cars, then DeepRacer will excite you. DeepRacer is a small autonomous race vehicle project where you can digitally control a real-life car based on reinforcement learning.
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Conclusion
Amazon Tools continues adding new machine learning tools and services based on new use cases every few months. While that is fascinating, the new additions and many choices would consume a lot of time as one would have to assess which is right for them. This is where Veritis come in.
Veritis, the Stevie and Globee Business Award winner, is one of the best IT consulting services that help clients to overcome critical business challenges. If your organization is looking forward to adopting the machine learning tools that Amazon offers, contact our Veritis team, whose expertise in AWS machine learning solutions will guide you to the best solution for your case.
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Additional Resources:
- AIOps Vs MLOps: Understanding Significant Differences
- What is Generative AI: An Ultimate Guide to Amazon Generative AI Tools
- What is MLOps? Why MLOps and How to Implement It
- Revolutionizing Software Development: The Power of MLOps
- AIOps Use Cases: How Artificial Intelligence is Reshaping IT Management
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