Artificial intelligence (AI) and machine learning (ML) have become indispensable technologies for businesses today. Major tech companies like Amazon, Microsoft, and Google offer robust cloud platforms with AI and ML capabilities to meet this growing demand. In this article, we’ll compare the AI/ML offerings of the top 3 public cloud providers – AWS, Azure, and GCP.
Amazon Web Services:
Amazon Web Services (AWS) was the first cloud computing platform to offer Infrastructure as a Service in 2008 and has been a leader in cloud-based services. AWS is the most mature and widely adopted cloud AI/ML platform. SageMaker provides end-to-end capabilities for building, training, deploying ML models. Tight integration with other AWS services like S3, Lambda, etc. Broad range of AI services like Lex, Rekognition, Translate. Leverages the ecosystem of small/medium businesses.
AWS provides a comprehensive set of AI and ML products and services including:
| AWS AI and ML API modules for services | ||
| AWS | API Module | Description |
| AI Services | Comprehend | Extract insights about the content of documents without the need of any special preprocessing. |
| Comprehend Medical | Comprehend Medical detects and returns useful information in unstructured clinical text. | |
| Forecast | A fully managed deep learning service for time series forecasting. | |
| Fraud Detector | A fully managed service that helps you detect suspicious online activities | |
| HealthLake | A Fast Healthcare Interoperability Resources (FHIR)-enabled patient Data Store. | |
| Kendra | A search service that enables users to search unstructured text using natural language. | |
| Lex | Building conversational interfaces into applications using voice and text. | |
| Lookout for Equipment | Provides a conceptual overview of Amazon Lookout for Equipment. | |
| Lookout for Metrics | Helps you continuously find anomalies in business and operational data based on the same technology used by Amazon.com. | |
| Lookout for Vision | Find visual defects in industrial products, accurately and at scale. | |
| Monitron | An end-to-end system that detects abnormal behaviour in industrial machinery. | |
| Panorama | Improve their operations by automating monitoring and visual inspection tasks at the edge. | |
| Personalize | Real-time personalization and recommendations. | |
| Polly | Text-to-Speech (TTS) cloud service that converts text into lifelike speech. | |
| Rekognition | Add image and video analysis to your applications. | |
| Textract | Add document text detection and analysis to your applications. | |
| Translate | Translate text to and from English across a breadth of supported languages. | |
| Transcribe | Provide transcription services for your audio files and audio streams. | |
| DeepComposer | An artificial intelligence (AI)-enabled music keyboard that provides you with a hands-on learning experience to explore generative learning. | |
| DeepLens | A connected HD camera developer kit with a set of sample projects to help developers learn machine learning concepts using hands-on computer vision use cases. | |
| AWS AI and ML API modules for platform functions | ||
| AWS | API Module | Description |
| AI Platform | SageMaker | Build and train machine learning models, and then deploy them into a production-ready hosted environment. |
| Augmented AI (A2I) | Build the workflows required for human review of ML predictions. | |
| DevOps Guru | Generates operational insights to help you improve the performance of your operational applications. | |
| Elastic Inference | Attach low-cost GPU-powered acceleration to many Amazon machine instances in order to reduce the cost of running deep learning inference. | |
| Deep Learning AMIs | Equip machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud at any scale. | |
| Deep Learning Containers | A set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. | |
| DeepRacer | A reinforcement learning (RL)-enabled autonomous 1/18th-scale vehicle with supporting services in the AWS Machine Learning ecosystem. | |
| Apache MXNet on AWS | An open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of platforms, from cloud infrastructure to mobile devices. | |
| Machine Learning | Build smart applications, including applications for fraud detection, demand forecasting, targeted marketing, and click prediction. | |
| CodeGuru | Provides intelligent recommendations for improving application performance, efficiency, and code quality in Java applications. | |
AWS AI and ML industry cases:
| Healthcare and life sciences | Unlock hidden potential in your health data with HIPAA-eligible ML for petabyte-scale analysis and fast unstructured text and speech documentation. |
| Financial services | Innovate with ML across banking, payments, capital markets, and insurance sectors to improve customer experience with personalization, and virtual assistants and prevent online fraud. |
| Industrial and manufacturing | Detect abnormal machine behavior, spot defects, enable predictive maintenance, and improve operations with purpose-built Industrial AI services with no ML experience required. |
AWS is the most well-rounded and widely used. A key advantage of AWS is how tightly its AI and machine learning services integrate with its broader platform and ecosystem. For example, it’s easy to leverage other AWS offerings like S3 storage and Lambda functions when building an end-to-end ML workflow. The large ecosystem of small and medium businesses that AWS has accumulated helps drive adoption of its AI/ML platform. Through its flywheel effect, AWS can gain more data and use cases by having a bigger customer base, which enables it to improve its artificial intelligence capabilities even further. In summary, AWS has established itself as a mature, full-featured, and widely used cloud platform for artificial intelligence and machine learning applications. The combination of tight internal integration and a thriving ecosystem give AWS a strong competitive position in cloud-based AI/ML.In summary, AWS provides a mature, full-featured, and widely adopted cloud platform for building and deploying AI/ML applications. The tight integration across AWS services and large ecosystem gives it a competitive advantage.
Microsoft Azure:
Microsoft Azure is another leading cloud platform that offers a comprehensive set of artificial intelligence and machine learning services. Azure’s machine learning platform enables building, training and deploying ML models and includes automated ML capabilities to quickly generate models with low code or no code. Azure Machine Learning seamlessly supports open source frameworks like PyTorch and TensorFlow. One standout feature of Azure is Azure Applied AI Services which provides prebuilt AI modules and solutions tailored for business scenarios like customer service, process automation and search. Azure AI platform also includes Azure Cognitive Services which offers APIs and services for computer vision, speech, language and decision-making.
Four parts of Azure AI and ML platform by functions:
| Azure AI/ML | Description |
| Azure Applied AI Services | Modernise business processes with task-specific AI |
| Accelerate development with built-in business logic | |
| Run responsibly with security both in the cloud and the edge | |
| Azure Cognitive Services | Easily access sophisticated AI models |
| Build with AI services to achieve CV,ASR, and NLP applications | |
| Deploy anywhere from the cloud to edge with containers | |
| Get started quickly and customize solutions | |
| Azure Machine Learning | Develop with your choice of tools |
| Create and deploy models at scale | |
| Innovate responsibly with built-in responsible capabilities | |
| Build your way for open-source frameworks and languages | |
| AI Infrastructure | Access large-scale infrastructure |
| Enable hybrid and multi-cloud deployments | |
| Access a breadth of AI hardware |
A key strength of Microsoft Azure is its deep focus on the enterprise segment and integration with other Microsoft products and services. Azure ML naturally leverages Microsoft’s expertise in operating systems, databases, Office products and other enterprise software. This allows it to deliver AI solutions tailored for heavily regulated industries like healthcare which have specific compliance needs. Azure also utilizes Microsoft’s strong foothold with large organizations to offer robust AI capabilities customized for companies with complex IT systems and workflows. Its hybrid cloud approach allows easy integration of public and private clouds. In the AI application scenario, the high-frequency applications based on the cloud and the edge are extracted: Machine Learning, Knowledge Mining, Conversation AI, Document process automation, Machine translation, and Speech transcription, Azure high frequency AI and ML scenarios based on the cloud and the edge:
| Make AI real | Description |
| Machine Learning | Build, train, and deploy models |
| Use the latest tools and frameworks | |
| Provide low-code and no-code tools | |
| Knowledge Mining | Uncover latent insights from documents, images, and media |
| The only cloud search service with built-in AI capabilities | |
| Conversation AI | Develop enterprise-grade conversational AI experiences |
| Build multilingual and multimodal bots for nearly any scenario | |
| Document process automation | Turn documents into usable data |
| Accelerate document processing | |
| Machine translation | Translate text and documents in real time or in batches |
| Speech transcription | Transcribe speech to text |
| Produce natural-sounding text-to-speech voices |
In terms of AI and ML industrial application, Azure has integrated the four industry solutions of financial services, manufacturing, retail, and healthcare after summarizing and refining several single-point solutions in the same industry. AI and ML industrial use cases:
| AI/ML industry module | Description |
| Financial services | Combat financial crime |
| Improve customer experiences | |
| Modernize core banking | |
| Manufacturing | Automate quality control |
| Practice proactive maintenance | |
| Enhance worker safety | |
| Retail | Improve customer experiences |
| Optimize customer assistance | |
| Optimize stock replenishment | |
| Healthcare | Enable predictive care |
| Improve operational outcomes | |
| Accelerate innovation |
In summary, Microsoft Azure provides an end-to-end AI/ML platform focused on ease of use, prebuilt solutions, and tight enterprise integration. Its automated ML and seamless support for open source frameworks lowers the barrier to AI adoption. And Microsoft’s long-standing relationships with large enterprises give Azure an advantage in bringing AI to complex, regulated industries. These factors contribute to Azure’s position as a top cloud AI/ML platform.
Google Cloud Platform:
Google Cloud Platform (GCP) takes a differentiated approach to providing AI and machine learning services compared to AWS and Azure. GCP offers an end-to-end machine learning platform called Vertex AI that supports the full model development lifecycle including tools and services for data preparation, training, deployment and monitoring. A highlight of Vertex AI is its feature stores for simplified data management for ML. GCP also provides AutoML services that leverage Google’s advanced transfer learning and neural architecture search technologies to allow developers with limited ML expertise to train high-quality custom models. A key advantage of GCP is its specialization in AI research and technologies. For example, it offers TensorFlow, a popular open-source ML framework that originated at Google, as well as Cloud TPUs or Tensor Processing Units tailored for ML workloads. GCP also provides a range of prebuilt AI services like video/image analytics, speech recognition, natural language processing and translation. Overall, GCP takes a cloud-first approach focused on providing ML developers advanced capabilities and flexibility.
| GCP AI and ML API modules for data scientists | ||
| GCP AI/ML | API Modules | Descriptions |
| AI for Data Scientists | Vertex AI | Accelerating data preparation |
| Scaling data | ||
| Training and experimentation | ||
| Model deployment | ||
| Vertex AI Workbench | Rapid prototyping and model development | |
| Developing and deploying AI solutions on Vertex AI with minimal transition | ||
| GCP AI and ML API modules for developers | ||
| GCP AI/ML | API Modules | Descriptions |
| AI for Developers | AutoML | Building custom machine learning models in minutes |
| Training models specific to your business needs | ||
| Cloud Inference API | Indexing and loading a dataset consisting of multiple stored data sources | |
| Executing Inference queries over loaded datasets | ||
| Unloading or canceling the loading of a dataset | ||
| Dialogflow | Creating natural interaction for complex multi-turn conversations | |
| Building and deploying advanced agents quickly | ||
| Building enterprise-grade scalability | ||
| Media Translation (Beta) | Delivering real-time speech translation directly from your audio data | |
| Scaling quickly with straightforward internationalization | ||
| Speech-to-Text | Creating automatic speech recognition | |
| Transcribing in real time | ||
| Empowering Google Contact Center AI | ||
| Text-to-Speech | Improving customer interactions | |
| Engaging users with voice user interface in devices and applications | ||
| Personalizing communication | ||
| Timeseries Insights API (Preview) | Gathering insights in real time from time series datasets | |
| Detecting anomalies while they are happening | ||
| Handling large scale datasets and running thousands of queries per second | ||
| Translation AI | Delivering seamless user experience with real-time translation | |
| Engaging your audience with compelling localization of your content | ||
| Reaching global markets through internationalization of your products | ||
| Video AI | Extracting rich metadata at the video, shot, or frame level | |
| Creating your own custom entity labels with AutoML Video Intelligence | ||
| Vision AI | Using ML to understand images with industry-leading prediction accuracy | |
| Training ML models to classify images by custom labels using AutoML Vision | ||
| GCP AI and ML API modules infrastructure | ||
| GCP AI/ML | API Modules | Descriptions |
| AI Infrastructure | Deep Learning Containers | Prototyping your AI applications in a portable and consistent environment |
| Deep Learning VM Image | Accelerating your model training and deployment | |
| GPUs | Speeding up compute jobs like machine learning and HPC | |
| Accelerating specific workloads on your VMs | ||
| TensorFlow Enterprise | Boosting enterprise development with long-term support on specific distributions | |
| Scaling resources across CPUs, GPUs, andCloud TPUs | ||
| Developing and deploying TensorFlow across managed services | ||
| TPUs | Running cutting-edge machine learning models with AI services on Google Cloud | |
| Iterating quickly and frequently on machine learning solutions | ||
| Building your own ML-powered solutions for real-world use cases | ||
Conclusion:
Google Cloud Platform takes a leading role in providing advanced artificial intelligence and machine learning technologies through its cloud platform. A core advantage of GCP is its extensive experience in AI research and deeply technical approach to developing AI/ML services. For example, TensorFlow, one of the most widely used open source ML frameworks, was originally developed at Google. GCP offer TensorFlow-based services as well as Cloud TPUs, its custom-designed hardware accelerators optimized for ML workloads. GCP provides Vertex AI, a unified end-to-end platform for building, deploying and managing ML models. Highlights of Vertex AI include its feature stores for managing data for ML in a scalable way. GCP also enables no-code or low-code ML model development through its AutoML technology which leverages Google’s expertise in transfer learning and neural architecture search. In addition, GCP offers pre-built AI services like computer vision, natural language processing, speech recognition and translation. While GCP’s footprint in enterprise AI is smaller than AWS and Azure, it aims to appeal to leading-edge ML developers and researchers through access to Google’s latest AI research and technologies. GCP’s API-based approach gives developers flexibility while making Google’s AI advancements available. In summary, GCP’s contributions center around providing ML developers with an advanced cloud platform to leverage state-of-the-art capabilities, tools, and Google’s extensive AI research – rather than more turnkey solutions tailored for business users.
In summary, AWS (Amazon Web Services) has established a first-mover advantage through continuous technological innovation, customer-centric product polishing, and the creation of socio-economic benefits. It offers services like Amazon Lookout for Equipment, which helps find anomalies in business and operational data, and Amazon Comprehend, which extracts insights from unstructured text . Azure (Microsoft) and GCP (Google) have learned from Amazon’s success and built their moats based on their company characteristics. They are relatively late market entrants but have made significant contributions to the industry. Azure offers services like Azure Machine Learning, a fully managed deep learning service for time series forecasting. GCP provides cloud-based AI and ML capabilities through its platform, with a focus on open API interfaces and their documentation. All three companies have contributed to the development of cloud-based AI and ML platforms, each with its own competitive strategy and focus. They have played a crucial role in shaping the industry and driving its continuous growth and innovation.
