Artificial Intelligence as a Service (AIaaS) is an AI offering that you can use to incorporate AI functionality without in-house expertise. It enables organizations and teams to benefit from AI capabilities with less risk and investment than would otherwise be required. In this article, you will learn about the different types of AIaaS, including examples from the top three leading cloud providers - Microsoft Azure, AWS, and GCP.
Types of AI as a Service
Multiple types of AIaaS are currently available. The most common types include:
- Cognitive computing APIs—APIs enable developers to incorporate AI services into applications with API calls. Popular services include natural language processing (NLP), knowledge mapping, computer vision, intelligent searching, and translation.
- Machine learning (ML) frameworks—frameworks enable developers to quickly develop ML models without big data. This allows organizations to build custom models appropriate for smaller amounts of data.
- Fully-managed ML services—fully-managed services can provide pre-built models, custom templates, and code-free interfaces. These services increase the accessibility of ML capabilities to non-technology organizations and enterprises that don’t want to invest in the in-house development of tools.
- Bots and digital assistance—including chatbots, digital assistants, and automated email services. These tools are popular for customer service and marketing and are currently the most popular type of AIaaS.
Why AI as a Service Can Be Transformational for AI Projects
In addition to being a sign of how far AI has advanced in recent years, AIaaS has several wider implications for AI projects and technologies. A few exciting ways that AIaaS can help transform AI are covered below.
Robust AI development requires a complex system of integrations and support. If teams are only able to use AI development tools on a small range of platforms, advancements take longer to achieve because fewer organizations are working on compatible technologies. However, when vendors offer AIaaS, they help development teams overcome these challenges and speed advances.
Several significant AIaaS vendors have already encouraged growth. For example, AWS in partnership with NVIDIA provides access to GPUs used for AIaaS. Or, Siemens and SAS, who have partnered to include AI-based analytics in Siemens’ Industrial Internet of things (IIoT) software. As these vendors implement AI technologies, they help standardize the environmental support of AI.
AIaaS eliminates much of the expertise and resources that are needed to develop and perform AI computations. This elimination can decrease the overall cost and increase the accessibility of AI for smaller organizations. This increased accessibility can drive innovation since teams that were previously prevented from using advanced AI tools can now compete with larger organizations.
Additionally, when small organizations are better equipped to incorporate AI capabilities, it is more likely to be adopted in previously lacking industries. This opens markets for AI that were previously inaccessible or unappealing and can drive the development of new offerings.
The natural cost curve of technologies decreases as resources become more widely available and demand increases. As demand increases for AIaaS, vendors can reliably invest to scale up their operations, driving down the cost for consumers. Additionally, as demand increases, hardware and software vendors will compete to produce those resources at a more competitive cost, benefiting AIaaS vendors and traditional AI developers alike.
AI as a Service Platforms
Currently, all three major cloud providers offer some form of AIaaS services.
Azure provides AI capabilities in three different offerings—AI Services, AI Tools and Frameworks, and AI Infrastructure. Microsoft also recently announced that it is going to make the Azure Internet of Things Edge Runtime public. This enables developers to modify and customize applications for edge computing.
AI Services include:
- Cognitive Services—enables users without machine learning expertise to add AI to chatbots and web applications It allows you to easily create high value services, such as chatbots with the ability to provide personalized content. Services include functionality for decision making, language and speech processing, vision processing, and web search improvements.
- Cognitive Search—adds Cognitive Services capabilities to Azure Search to enable more efficient asset exploration. This includes auto-complete, geospatial search, and optical character recognition (OCR).
- Azure Machine Learning (AML)—supports custom AI development, including the training and deployment of models. AML helps make ML development accessible to all levels of expertise. It enables you to create custom AI to meet your organizational or project needs.
AI Tools & Frameworks include Visual Studio tools, Azure Notebooks, virtual machines optimized for data science, various Azure migration tools, and the AI Toolkit for Azure IoT Edge.
Amazon Web Services (AWS)
Amazon offers AI capabilities focused on AWS services and its consumer devices, including Alexa. These capabilities overlap significantly since many of AWS’ cloud services are built on the resources used for its consumer devices.
AWS’ primary services include:
- Amazon Lex—a service that enables you to perform speech recognition, convert speech to text, and apply natural language processing to content analysis. It uses the same algorithm currently used in Alexa devices.
- Amazon Polly—a service that enables you to convert text to speech. It uses deep learning capabilities to deliver natural-sounding speech and real-time, interactive “conversation”.
- Amazon Rekognition—a computer vision API that you can use to add image analysis, object detection, and facial recognition to your applications. This service uses the algorithm employed by Amazon to analyze Prime Photos.
Google has made serious efforts to market Google Cloud as an AI-first option, even rebranding its research division as “Google AI”. They have also invested in acquiring a significant number of AI start-ups, including DeepMind and Onward. All of this is reflected in their various offerings, including:
- AI Hub—a repository of plug-and-play components that you can use to experiment with and incorporate AI into your projects. These components can help you train models, perform data analyses, or leverage AI in services and applications.
- AI building blocks—APIs that you can incorporate into application code to add a range of AI capabilities, including computer vision, NLP, and text-to-speech. It also includes functions for working with structured data and training ML models.
- AI Platform—a development environment that you can use to quickly and easily deploy AI projects. Includes a managed notebooks service, VMs and containers pre-configured for deep learning, and an automated data labeling service.
Cloud computing vendors and third party service providers continue to extend capabilities into more realms, including AI and machine learning. Today, there are cognitive computing APIs that enable developers to leverage ready-made capabilities like NLP and computer vision. If you are into building your own models, you can use machine learning frameworks to fast-track development.
There are also bots and digital assistants that you can use to automate various services. Some services require configuration, but others are fully-managed and come with a variety of licensing. Be sure to check the shared responsibility model offered by your provider, to ensure that you are fully compliant with regulatory requirements.
This is a companion discussion topic for the original entry at https://blog.datasciencedojo.com/artificial-intelligence-as-a-service/