AIaaS : What is Artificial Intelligence as a Service, Type and Platform

Artificial Intelligence as a Service is an AI offering you can use to integrate AI functionality without having to have in-house expertise. This allows teams and organizations to reap the benefits of AI with less risk and investment than is normally required.

What is AIaaS (Artificial intelligence as a Service)?

Artificial intelligence as a Service refers to pre-made AI tools that allow companies to scale up and implement AI techniques at a fraction of the cost of an in-house AI.

Everything as a Service is any software that can be used across a network. It relies on Cloud Computing. The software is usually available pre-purchased. It is easy to buy the software from a third-party vendor and make some tweaks. You can start using it almost immediately even though it hasn’t been customized for your system.

For a long period, artificial intelligence was prohibitively expensive for most companies.

• These machines were large and costly.
• These programmers were hard to find (which led to high salaries).
• Many companies did not have enough data to study.
• Cloud services are becoming more accessible and AI is easier to access. Companies can store unlimited data. AI-as a service is the answer.

Also read: Security as a Service: A Definition of SECaaS, Benefits, and Examples

Types of AI as a service

There are many types of AIaaS currently available. These are the most popular types:

• Cognitive computing APIs — Developers can use APIs to integrate AI services into their applications. Natural language processing ( NLP), knowledge map, intelligent searching, and translation are some of the most popular services.
• Machine learning (ML) frameworks — Frameworks allow developers to quickly create ML models without big data. This allows organizations to create custom models that can be used with smaller amounts of data.
• Fully-managed ML services — Fully-managed services offer pre-built models and custom templates as well as code-free interfaces. These services make it easier for non-technical organizations and businesses to access ML capabilities.
• Bots and digital assistance — Chatbots, digital assistants and automated email services are all examples. These tools are very popular in customer service and marketing. They are currently the most used type of AIaaS.

AI as a Service: Why AI can be Transformational for AI Projects

This is a sign that AIaaS is a good indicator of the progress AI has made in recent years. It also has many wider implications for AI projects. Below are some of the exciting ways AIaaS could help transform AI.

Ecosystem growth

Complex integrations and support are required for robust AI development. Because fewer companies are using compatible technologies, it takes longer for advancements to be made if teams can only use AI development tools on limited platforms. Vendors offering AIaaS help developers overcome these obstacles and accelerate their progress.

Many AIaaS providers have encouraged growth. AWS, in partnership with NVIDIA, provides GPUs for AI as a service. Siemens and SAS have partnered to integrate AI-based analytics into Siemens’ Industrial Internet of Things ( IIoT ) software. These vendors help to standardize AI support by implementing AI technologies.

Accessibility increased

AI as a service eliminates much of what expertise and resources are required to develop and execute AI computations. This can reduce the cost of AI and make it more accessible for smaller companies. This accessibility allows for innovation as teams previously unable to use advanced AI software are now able to compete with larger companies.

Smaller organizations that are more equipped to integrate AI capabilities will be more likely to adopt it in industries that have not yet adopted it. This creates new markets for AI, which can be difficult or unavailable previously.

Lower cost

As resources become more readily available and demand rises, the natural cost curve for technologies falls. Vendors can invest in scale-up operations to meet the increasing demand for AIaaS. This helps lower the cost of AIaaS for consumers. Hardware and software vendors will be able to compete for these resources at a lower cost as they grow in demand, which will benefit both traditional AI developers and AIaaS vendors.

AI as a Service Platform

All three of the major cloud providers currently offer Artificial intelligence as a Service.

1. Microsoft Azure

Azure offers three types of AI capabilities: AI Services, AI Tools and Frameworks, and AI Infrastructure. Microsoft recently announced that the Azure Internet Of Things Edge Runtime will be made public. This allows developers to customize and modify applications for edge computing.

AI Services include:

• Cognitive Service–allows users with no machine learning skills to add AI to chatbots or web applications. It allows you to create high-value services such as chatbots that can provide personalized content and chatbots. These services include functionality for decision-making, language and speech processing, vision processing and web search improvements.
• Cognitive Search–adds Cognitive Services capabilities for Azure Search to improve asset exploration. This includes geospatial search, auto-complete, and optical character recognition (OCR).
• Azure Machine Learning–Supports custom AI development, including training and deployment. AML makes ML development more accessible for all levels of expertise. You can create AI that is tailored to your project or organizational needs.

AI Tools & Frameworks include Visual Studio tools, Azure notebooks, virtual machines optimized for data science, various Azure migration tools, as well as the AI Toolkit For Azure IoT Edge.

Also read: How to Choose the Right EiPaaS Platform

2. Amazon Web Services (AWS)

Amazon provides AI capabilities that are focused on AWS services as well as its consumer devices, such as Amazon Alexa. These capabilities are closely related since many AWS cloud services are built using resources from its consumer devices.

AI Services include:

• Amazon Lex – A service that allows you to recognize speech, convert speech into text, and use natural language processing for content analysis. It uses the same algorithm as Alexa devices.
• Amazon Polly – A service that allows you to convert text into speech It makes use of deep learning to produce natural-sounding speech as well as interactive, real-time “conversations”.
• Amazon Rekognition – A computer vision API you can use to add facial recognition, object detection, and image analysis to your applications. This service uses Amazon’s algorithm to analyze Prime Photos.

3. Google Cloud

Google Cloud has been aggressively promoted by Google, including renaming its research division “Google AI”. They also invested in the acquisition of a large number of AI start-ups including DeepMind, Onward, and others. They offer a variety of offerings that reflect this, including:

AI Services include:

• AI Hub – A repository of plug-and-play components that you can use for experimentation and incorporate AI into your projects. These components are useful for training models, performing data analysis, and leveraging AI in services or applications.
• AI building blocks – APIs you can add to your application code to increase a variety of AI capabilities including text-to-speech and NLP. It also contains functions to work with structured data and ML models.
• AI Platform – A development environment you can use to quickly deploy AI projects. This includes a managed notebook service and pre-configured containers and VMs for deep learning. There is also an automated data labeling service.

Conclusion

Third-party service providers and cloud computing vendors continue to expand their capabilities in more areas, including AI/machine learning. Cognitive computing APIs allow developers to use pre-made capabilities such as NLP or computer vision. Machine learning frameworks can be used to accelerate development if you’re interested in building your own models.

You can also use bots and digital assistants to automate different services. While some services may require configuration, others can be fully managed and come with a range of licensing options. To ensure compliance with regulatory requirements, make sure you check the shared responsibility model provided by your provider.