Artificial intelligence is evolving by the day, making it hard for small and medium businesses to stay competitive. Luckily, three tech giants – Microsoft, Amazon and Google – are providing a range of cloud-based AI services that are cost efficient and offer the latest tech.
Artificial intelligence (AI) enables businesses to automate tasks, enhance customer experience, optimise operations, and generate insights from data. However, developing and deploying AI solutions can be challenging and costly, especially for small and medium enterprises (SMEs) that lack the resources and expertise to build their own AI infrastructure and applications.
As infrastructure requirements for modern AI solutions like deep learning and generative AI are high, new age solutions are expensive. Cloud-based solutions have therefore become the de-facto choice for many organisations.
Fortunately, there are cloud-based AI services that can help businesses leverage the power of AI without the need to invest in hardware, software, or personnel. These services provide ready-to-use AI capabilities, such as computer vision, natural language processing, speech recognition, machine learning, and more, that can be integrated into various applications and platforms. Some of the leading cloud providers that offer AI services are Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Here’s an overview of the AI services offered by these three providers.
Azure AI services
Azure is Microsoft’s cloud computing platform that provides a range of AI services for various industries and use cases. Some of the key services it offers are listed below.
- Azure cognitive services
This is a collection of APIs and SDKs that enable developers to add cognitive capabilities, such as vision, speech, language, knowledge, and search, to their applications. For example, Azure Computer Vision API can analyse images and videos for content, emotion, face detection, object recognition, and more. Azure Speech Services can convert speech to text and text to speech, as well as translate speech across languages. Azure Language Understanding (LUIS) can extract intent and entities from natural language queries and commands. Azure QnA Maker can create conversational agents that can answer questions based on a knowledge base.
- Azure Machine Learning
This cloud-based platform enables data scientists and developers to build, train, deploy, and manage machine learning models at scale. Azure Machine Learning supports various frameworks and tools, such as PyTorch, TensorFlow, scikit-learn, and ONNX. It also provides automated machine learning, which can generate optimal models based on the data and the problem type. Azure Machine Learning Studio is a graphical interface that allows users to create and deploy machine learning models without coding.
- Azure Bot Service
This service enables developers to create and deploy intelligent chatbots that can interact with users across various channels, such as websites, mobile apps, social media, and voice assistants. Azure Bot Service supports various bot frameworks and languages, such as Bot Framework SDK, Bot Framework Composer, and Bot Framework Adaptive Dialog. It also integrates with Azure Cognitive Services, such as LUIS and QnA Maker, to provide natural language understanding and knowledge base capabilities for the chatbots.
- Azure Databricks
This service provides a unified analytics platform based on Apache Spark, a popular open source framework for large-scale data processing and machine learning. Azure Databricks enables users to run interactive queries, stream analytics, and run machine learning workloads on massive datasets. It also integrates with various Azure services, such as Azure Data Lake Storage, Azure Synapse Analytics, Azure Machine Learning, and Azure DevOps, to provide a comprehensive data and AI solution.
Azure AI services can be used for various industries, such as banking and financial services, healthcare and medical services, learning and development (education), retail and e-commerce.
Azure OpenAI services
Azure OpenAI services are a set of cloud-based AI services that leverage the cutting-edge research and technology of OpenAI, the leading AI research organisation. These services enable developers and data scientists to build and deploy intelligent applications that can understand natural language, generate realistic text, analyse images and videos, and more (see Table 1).
Table 1: The usage of various Azure OpenAI services
| Azure OpenAI service | Usage |
| Azure OpenAI Codex | Can convert natural language into code, queries, formulas, and other executable commands for various programming languages and platforms. |
| Azure OpenAI GPT-3 | Can generate natural language text for a wide range of tasks and domains, such as summarisation, translation, chat, and content creation. |
| Azure OpenAI Vision | Can perform image and video analysis, such as object detection, face recognition, scene understanding, and captioning. |
| Azure OpenAI Conversational AI | Can create engaging and natural conversational agents, such as chatbots, voice assistants, and social media bots. |
Table 2: A comparison of selected AWS, Azure and GCP cloud-based AI services
| Service category | AWS | Azure | GCP |
| Pre-trained AI services | AWS offers a range of pre-trained AI services for various domains, such as vision, speech, language, chatbots, forecasting, and recognition. Examples are Amazon Rekognition, Amazon Polly, Amazon Comprehend, Amazon Lex, Amazon Forecast, and Amazon Rekognition. | Azure offers a range of pre-trained AI services for various domains, such as vision, speech, language, decision, and web search. Some examples are Azure Cognitive Services, Azure Speech Services, Azure Language Understanding, Azure Personalizer, and Azure Cognitive Search. | GCP offers a range of pre-trained AI services for various domains, such as vision, speech, language, translation, natural language, video, and recommendations. Examples include Google Cloud Vision API, Google Cloud Speech-to-Text, Google Cloud Translation, Google Cloud Natural Language, Google Cloud Video Intelligence, and Google Cloud Recommendations AI. |
| Custom AI services | AWS’s range of custom AI services allow users to build, train, and deploy their own machine learning models. Examples are Amazon SageMaker, Amazon Kendra, Amazon Textract, Amazon Fraud Detector, and Amazon Augmented AI. | Azure’s custom AI services also allow users to build, train, and deploy their own machine learning models. Some examples are Azure Machine Learning, Azure Form Recognizer, Azure Anomaly Detector, Azure Synapse Analytics, and Azure Machine Learning Designer. | GCP, too, offers a range of custom AI services that allow users to build, train, and deploy their own machine learning models. Examples include Google Cloud AI Platform, Google Cloud AutoML, Google Cloud Document AI, Google Cloud Dataflow, and Google Cloud Vertex AI. |
| Data analytics and processing | AWS’s data analytics and processing services enable users to collect, store, analyse, and visualise large-scale data. Examples are Amazon Kinesis, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon QuickSight. | Azure also offers a range of data analytics and processing services that enable users to collect, store, analyse, and visualise large-scale data. Some examples are Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure HDInsight, and Azure Power BI. | GCP’s data analytics and processing services, too, enable users to collect, store, analyse, and visualise large-scale data. Examples are Google Cloud Pub/Sub, Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, and Google Data Studio. |
Azure OpenAI services are integrated with the Azure platform, which provides a comprehensive and secure cloud environment for building, deploying, and managing AI solutions. You can use Azure cognitive services to complement Azure OpenAI services with additional functionality and features.
By using Azure OpenAI services in the Azure platform, you can enjoy the following benefits.
Access to state-of-the-art AI: You can leverage the latest advancements in AI research and technology from OpenAI, which is constantly pushing the boundaries of natural language and computer vision.
Easy and flexible integration: You can easily and flexibly integrate Azure OpenAI services with your existing applications and workflows, using various Azure services and tools that suit your needs and preferences.
High performance and scalability: High performance and scalability can be achieved for your AI solutions using the powerful and reliable cloud infrastructure of Azure, which can handle large volumes of data and requests.
Security and compliance: You can ensure the security and compliance of your AI solutions using the robust and comprehensive security features and policies of Azure, which can protect your data and resources from unauthorised access and threats.
Cost-effectiveness and efficiency: The cost-effectiveness and efficiency of your AI solutions can be optimised using the pay-as-you-go and consumption-based pricing models of Azure, which can help you save money and resources.
Table 2 compares some of the AI services offered by AWS, Azure, and GCP.
AWS AI services
Amazon has emerged as a trailblazer with its comprehensive suite of AI services. Harnessing the power of advanced machine learning algorithms, scalable cloud infrastructure, and deep integration capabilities, Amazon AI services deliver robust solutions for complex problems.
- Amazon SageMaker
Amazon SageMaker is a fully managed machine learning service that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker provides a suite of tools including managed Jupyter Notebooks for exploratory data analysis, built-in high-performance algorithms, and support for custom model development with Docker containers. It offers distributed training and hyperparameter optimisation, and allows seamless deployment of models to production for real-time and batch inference. SageMaker also includes features such as SageMaker Ground Truth for data labelling and SageMaker Studio, an integrated development environment for machine learning.
- Amazon Rekognition
Amazon Rekognition is a computer vision service that analyses images and videos to identify objects, people, text, scenes, and activities. It leverages deep learning models to provide functionalities like facial analysis, celebrity recognition, and detection of explicit content. Rekognition can perform real-time analysis on video streams and supports custom labels for training custom models. It integrates with other AWS services like Lambda and S3, making it suitable for security surveillance, automated content moderation, and image search and organisation.
- Amazon Lex
Amazon Lex is a service for building conversational interfaces using voice and text, powered by the same deep learning technologies that underpin Amazon Alexa. Lex provides automatic speech recognition (ASR) to convert speech to text and natural language understanding (NLU) to recognise the intent of the text. It supports multi-turn conversations and context management, enabling the creation of sophisticated chatbots and voice assistants. Lex integrates seamlessly with AWS Lambda for executing business logic and can be connected to other AWS services to build comprehensive, interactive applications.
- Amazon Polly
Amazon Polly is a text-to-speech (TTS) service that converts text into lifelike speech using advanced deep learning technologies. Polly offers a wide range of voices and languages, providing high-quality speech synthesis with low latency suitable for real-time applications. It supports Speech Synthesis Markup Language (SSML) for fine-grained control over speech output, including pronunciation, volume, and speech rate adjustments. Polly is widely used in e-learning platforms, content accessibility tools, and voice-enabled devices to enhance user engagement through natural-sounding speech.
- Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights from text. It can identify entities, perform sentiment analysis, detect languages, classify documents, and analyse relationships between entities. Comprehend supports custom entity recognition and topic modelling, allowing users to train models specific to their domain. It integrates with other AWS services for seamless data processing and is utilised in customer feedback analysis, content categorisation, and knowledge discovery to drive business intelligence.
- Amazon Transcribe
Amazon Transcribe is an automatic speech recognition (ASR) service that converts spoken language into text. It provides real-time and batch transcription with features like speaker identification and custom vocabulary for domain-specific terms. Transcribe is designed to handle various audio formats and noisy environments, delivering high accuracy in transcription. It is used in industries such as contact centres for call analysis, media for generating subtitles, and legal and healthcare sectors for documentation purposes, enhancing efficiency and accuracy in transcription workflows.
- Amazon Translate
Amazon Translate is a neural machine translation service that delivers fast, high-quality translations across numerous languages. It uses advanced deep learning models to provide accurate and fluent translations, supporting real-time and batch processing. Translate allows customisation with custom terminology, ensuring consistency in translating domain-specific terms. It integrates with other AWS services to enable seamless localisation of applications, multilingual customer support, and global communication, helping businesses expand their reach by overcoming language barriers.
- Amazon Textract
Amazon Textract is a machine learning service that automatically extracts text, handwriting, and data from scanned documents. It goes beyond simple OCR by identifying the structure of documents, such as forms and tables, to extract data accurately. Textract supports integration with other AWS services for automated processing and analysis of extracted data. It is used in digitising paper records, automating data entry, and ensuring regulatory compliance through accurate extraction and analysis of information from documents, streamlining business operations.
- Amazon Kendra
Amazon Kendra is an intelligent search service that uses machine learning to deliver highly accurate and relevant search results. It understands natural language queries and indexes content from various data sources, providing contextual and relevant information. Kendra allows customisation of search relevance based on user feedback and supports features like document ranking and faceted search. It is utilised in enterprise search applications, enhancing knowledge management systems, customer support portals, and any scenario requiring efficient and accurate information retrieval.
GCP AI services
Google Cloud Platform (GCP) offers a wide range of AI services designed to help developers and businesses incorporate machine learning and artificial intelligence into their applications.
Cloud AI Platform
This is a comprehensive suite of tools for building, deploying, and managing machine learning models at scale. It comprises:
AI platform notebooks: These manage Jupyter Notebooks for ML development.
AI platform training: Scalable training of ML models using various frameworks.
AI platform prediction: Scalable serving of ML models for online predictions.
AI platform pipelines: These orchestrate ML workflows using Kubeflow pipelines.
- AutoML
The tools under this service enable developers with limited ML expertise to train high-quality models.
AutoML Vision: Enables custom image classification and object detection models.
AutoML Video Intelligence: Trains models for video classification and object tracking.
AutoML Natural Language: Helps with custom models for text classification, sentiment analysis, and entity extraction.
AutoML Translation: Builds custom translation models for specific language pairs.
AutoML Tables: Trains ML models on structured data.
- Vision AI
This comprises tools for deriving insights from images and videos using powerful ML models.
Cloud Vision API: Has image recognition and understanding capabilities.
Video Intelligence API: Enables video analysis for detecting objects, actions, and scenes.
- Natural Language AI
This offers services for analysing and understanding text.
Cloud Natural Language API: Helps with text analysis, including sentiment analysis, entity recognition, and syntax analysis.
AutoML Natural Language: Enables custom text classification and entity extraction models.
- Translation AI
This is a service for translating text between languages.
Cloud Translation API: Enables fast and dynamic translation services.
AutoML Translation: Trains custom translation models for specific domains.
- Dialogflow
This is a platform for building conversational interfaces and chatbots.
Dialogflow CX: Offers advanced conversational capabilities for complex use cases.
Dialogflow ES: This is a basic version for simpler chatbot applications.
- Speech-to-Text
This service helps convert spoken language into written text with high accuracy. The Cloud Speech-to-Text API enables the transcription of audio with support for multiple languages and custom vocabularies.
- Text-to-Speech
This converts text into natural-sounding speech. The Cloud Text-to-Speech API enables various voices and languages to synthesise human-like speech.
- Recommendations AI
This service enables personalised product recommendations tailored to individual users. It is a real-time recommendation engine for e-commerce and content platforms.
- AI Hub
This centralised repository is for sharing and discovering AI components. It’s a collaborative platform to share and find AI pipelines and components.
- AI Building Blocks
This service offers pre-trained ML models to quickly add AI capabilities to applications.
Vision AI: Pre-trained models for image and video analysis.
Natural Language AI: Pre-trained models for text analysis.
Translation AI: Pre-trained models for language translation.
- Deep Learning VM Image
Pre-configured VM images optimised for deep learning are offered by this service. These VMs have pre-installed ML frameworks and tools.
- TensorFlow Enterprise
This GCP service offers enterprise-grade support and tools for TensorFlow, including optimised TensorFlow runtime and long-term support.
These AI services from Google Cloud Platform provide powerful tools for developers to incorporate advanced machine learning and AI capabilities into their applications, offering both pre-trained models for common use cases and customisable models for specific business needs.
To sum up, the cloud-based AI services offered by Microsoft, Amazon and Google enable businesses to align their infrastructure with their requirements. The good thing is that they are cost efficient and help organisations stay in touch with the evolving tech.
