There are many cloud computing platforms that provide these web services for machine learning. The most popular of these are Amazon Web Services, Microsoft Azure, Google Cloud, and IBM Cloud. These are the oldest and most mature platforms that provide various products for Machine Learning ranging from natural language processing, service bots, and even deep learning.
Amazon Web Services is a cloud computing platform that is a subsidiary of Amazon. It was launched in 2006 is currently one of the most popular cloud computing platforms for machine learning.
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| Service | Description |
|---|---|
| SageMaker | Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly build and train machine learning models, and then deploy them into a production-ready hosted environment. |
| Augmented AI | Amazon Augmented AI (Amazon A2I) enables you to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers. |
| Comprehend | Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents without the need of any special preprocessing. Amazon Comprehend processes any text files in UTF-8 format. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. |
| Forecast | Amazon Forecast is a fully managed deep learning service for time-series forecasting. By providing Amazon Forecast with historical time-series data, you can predict future points in the series. Time-series forecasting is useful in multiple domains, including retail, financial planning, supply chain, and healthcare. |
| Kendra | Amazon Kendra is a search service, powered by machine learning, that enables users to search unstructured text using natural language. |
| Lex | Amazon Lex is an AWS service for building conversational interfaces into applications using voice and text. With Amazon Lex, the same deep learning engine that powers Amazon Alexa is now available to any developer, enabling you to build sophisticated, natural language chatbots into your new and existing applications. |
| Polly | Amazon Polly is a Text-to-Speech (TTS) cloud service that converts text into lifelike speech. You can use Amazon Polly to develop applications that increase engagement and accessibility. |
| Rekognition | Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Amazon Rekognition API, and the service can identify objects, people, text, scenes, and activities. It also provides highly accurate facial analysis and facial recognition |
| DeepRacer | AWS DeepRacer is a reinforcement learning (RL)-enabled autonomous 1/18th-scale vehicle with supporting services in the AWS Machine Learning ecosystem. It offers an interactive learning system for users of all levels to acquire and refine their skill set in machine learning in general and reinforcement learning in particular. |
Microsoft Azure is a cloud computing platform created by Microsoft. It was initially released in 2010 and is a popular cloud computing platform for machine learning and data analytics.
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| Service | Description |
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| Machine Learning | A cloud-based environment you can use to train, deploy, automate, manage, and track ML models. Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. |
| Bot Service | Azure Bot Service provides an integrated environment that is purpose-built for bot development. |
| Cognitive Search | Azure Cognitive Search (formerly known as "Azure Search") is a cloud search service that gives developers an architecture, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. |
| Computer Vision | The cloud-based Computer Vision API provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Microsoft Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices. |
| Language Understanding | Language Understanding (LUIS) is a cloud-based conversational AI service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. |
| Personalizer | Azure Personalizer is a cloud-based service that helps your applications choose the best content item to show your users. You can use the Personalizer service to determine what product to suggest to shoppers or to figure out the optimal position for an advertisement. After the content is shown to the user, your application monitors the user's reaction and reports a reward score back to the Personalizer service. |
| Kinect DK | Azure Kinect DK is a developer kit with advanced AI sensors that provide sophisticated computer vision and speech models. Kinect contains a depth sensor, spatial microphone array with a video camera, and orientation sensor as an all in-one small device with multiple modes, options, and software development kits (SDKs). |
The Google Cloud Platform is a cloud computing platform that is provided by Google. It was launched in 2008 and it provides the same infrastructure for companies that Google also uses in its internal products.
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| Service | Description |
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| Vertex AI | This new unified machine learning platform will help you build, deploy and scale more effective AI models. |
| AI building blocks | Easily add sight, language, conversation, and structured data into your applications. |
| AutoML | Use Vertex AI's capabilities for vision, translation, and structured data powered by AutoML, to train high-quality custom machine learning models with minimal effort and machine learning expertise. |
| AI Infrastructure | Train deep learning and machine learning models cost-effectively. |
| Conversational AI | This includes Speech-to-Text, Text-to-Speech, Virtual Agents and Dialogflow CX |
| Natural Language | Derive insights from unstructured text using Google machine learning. |
| Recommendations AI | Deliver highly personalized product recommendations at scale. |
| Vision OCR | Extract text from documents with world-class accuracy, supporting over 200 languages and 50 languages for handwriting recognition. |
| Media Translation | Media Translation API delivers real-time speech translation to your content and applications directly from your audio data. |
The IBM Cloud Platform is a cloud computing platform offered by IBM. It provides various cloud delivery models that are public, private, and hybrid models.
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| Service | Description |
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| Watson Studio | Watson Studio democratizes machine learning and deep learning to accelerate infusion of AI in your business to drive innovation. Watson Studio provides a suite of tools and a collaborative environment for data scientists, developers and domain experts. |
| Watson Speech-to-Text | This technology enables fast and accurate speech transcription in multiple languages for a variety of use cases, including but not limited to customer self-service, agent assistance and speech analytics. |
| Watson Text-to-Speech | This is an API cloud service that enables you to convert written text into natural-sounding audio in a variety of languages and voices within an existing application or within Watson Assistant. |
| Watson Natural Language Understanding | Watson Natural Language Understanding is a cloud native product that uses deep learning to extract metadata from text such as entities, keywords, categories, sentiment, emotion, relations, and syntax. |
| Watson Visual Recognition | Visual Recognition service uses deep learning algorithms to analyze images for scenes, objects, faces, and other content. |
| Watson Assistant | Watson Assistant provides customers with fast, consistent and accurate answers across any application, device or channel. |