What Is Artificial Intelligence as a Service?
AI-as-a-Service (AIaaS) is a cloud computing service that allows businesses and individuals to pay for artificial intelligence (AI) capabilities through a subscription or usage-based delivery model. This type of service is intended for companies and individuals who do not have the expertise, financial resources, infrastructure, or desire to build and deploy AI systems in-house.
AI cloud services vary in terms of the technical expertise required to use them. Some providers offer low-code/no-code (LCNC) services that hide the complexities of AI model development and deployment behind a user-friendly, drag and drop interface.
Most providers, however, expose application programming interfaces (APIs) to give customers more granular control over the provider’s services.
Types of Services
AIaaS providers offer a wide variety of services designed to make it easier to incorporate AI into business operations. Examples of popular services include:
- Machine-learning-as-a-service tools for developing, training, and deploying machine learning (ML) models;
- Natural language processing (NLP) services that can understand, interpret, and generate human language in a useful and meaningful way;
- Speech recognition and generation services that can convert spoken language into written text (Speech-to-Text) and vice versa (Text-to-Speech);
- Computer vision services that can analyze images and videos to identify objects, faces, or actions.
- Recommendation services that can analyze user behavior and preferences to provide personalized recommendations;
- Predictive analytics services that can analyze historical data to make predictions about future events;
- Data pre-processing services that can help with tasks like data cleaning, labeling, and transformation.
- AutoML services that automate the process of training and optimizing a machine learning model;
- Robot process automation (RPA) services that can automate repetitive, rule-based tasks.
AIaaS Provider and Customer Responsibilities
AI-as-a-Service democratizes access to AI by making the technology more readily available and affordable for organizations of all sizes. The division of responsibilities between the provider and the customer will vary depending on the exact nature of the service and the service-level agreement (SLA).
For example, some services provide customers with a selection of AI models to choose from, while others require the customer to handle model development themselves.
The chart below provides an overview of typical provider and customer responsibilities.
|AIaaS Service Provider Responsibilities||AIaaS Customer Responsibilities|
|Provide a reliable AI service that meets the specifications described in the service level agreements.||Source, clean, label, and maintain the data that will be used to train the AI model.|
|Manage the underlying infrastructure that supports the AI service. This includes server maintenance, hardware upgrades, and ensuring adequate computation resources.||Select (or build) the right AI model for the task at hand and then deploy it.|
|Provide customers with documentation and training resources that teach them how to use the service.||Continuously monitor the performance of the AI model and fine-tune or retrain as necessary.|
|Ensure the AI services they offer meet relevant cybersecurity standards and any other industry-specific compliance requirements.||Ensure that training data complies with all relevant privacy laws and regulations.|
|Make sure the AI service they offer is reliable and up-to-date with new advancements.||Troubleshoot common issues.|
Popular AIaaS vendors include:
- OpenAI: OpenAI, the organization behind ChatGPT, offers generative AI services and APIs that enable developers to integrate natural language processing capabilities into their applications, including text generation, language translation, and sentiment analysis.
- Amazon Web Services (AWS): AWS offers various AI services, including Amazon Rekognition for computer vision, Amazon Comprehend for natural language processing, Amazon Lex for building chatbots, and Amazon Forecast for predictive analytics.
- Google Cloud Platform (GCP): GCP provides AIaaS offerings such as Google Cloud Vision for image recognition, Google Cloud Natural Language for text analysis, and Google AutoML for training machine learning models.
- Microsoft Azure: Microsoft Azure offers a range of AI services, including Azure Cognitive Services for vision, speech, language, and search functionalities. Azure Machine Learning allows users to build, deploy, and manage machine learning models.
- IBM Watson: IBM Watson provides AI services such as Watson Assistant for building conversational agents and Watson Natural Language Understanding for text processing.
- Salesforce: Salesforce is an AI-powered platform that provides various AI services integrated into Salesforce’s customer relationship management (CRM) solutions. It includes features like predictive lead scoring, automated email responses, and sentiment analysis.
- Oracle AI Platform: Oracle AI Platform offers a suite of AIaaS solutions, including Oracle Autonomous Database with built-in machine learning capabilities, Oracle Cloud Data Science for building and deploying models, and Oracle AI Apps for industry-specific AI applications.
- Tencent AI Open Platform: Tencent, a Chinese technology company, offers an AI open platform that provides a wide range of AI capabilities, including image recognition, natural language processing, voice recognition, and recommendation systems.
- Baidu AI Open Platform: Baidu, a leading Chinese search engine, provides an open platform that offers AI services like image recognition, speech synthesis, natural language processing, and machine learning tools.
- Clarifai: Clarifai is an AI company that offers a platform for visual recognition and image analysis. It provides APIs and software development kits (SDKs) for tasks such as object detection, image classification, and facial recognition.