Artificial Intelligence as a Service (AlaaS) helps organizations incorporate artificial intelligence (AI) functionality without the associated expertise. Usually, AIaaS services are built upon cloud-based providers like Amazon AWS, Google Cloud, Microsoft Azure, and IMB Cloud, used as IaaS. The AI service, framework, and workflows built upon these infrastructures are offered to final customers for various use cases (e.g., inventory management services, manufacturing optmizations, text generation).
Understanding Artificial Intelligence as a Service
Artificial Intelligence as a Service allows businesses to experiment with artificial intelligence in a low risk environment and without a significant upfront investment.
AlaaS is a more recent addition to a suite of “as a service” products that help businesses maintain a focus on their core operations. It is becoming increasingly popular, with the International Data Corporation predicting that 75% of commercial enterprise applications will use AI in the coming years. As a result, large organizations such as Amazon, Google, IBM, and Microsoft all now offer AlaaS to customers.
To understand this industry, it’s important to understand its various layers. Just like SaaS, built on top of IaaS, PaaS, also AIaaS, is built on top of cloud infrastructure that works as the basis for the service itself.
The various types of AlaaS
AlaaS is a relatively broad term that can be divided into distinct types:
- Cognitive computing APIs – where an application programming interface (API) developer can utilise API calls to incorporate artificial intelligence into applications. This encompasses a range of services including computer vision, knowledge mapping, and natural language processing (NLP). Each has the ability to generate business value from unstructured information.
- Bots and digital assistance – a very popular form of AlaaS including automated email services, chatbots, and digital customer service agents.
- Fully-managed machine learning services – ideally suited to non-technological organizations who desire a fully managed approach. These services invariably offer customer templates and pre-built models. For the most technologically-challenged, they also offer code-free interfaces.
- Machine learning frameworks – or frameworks that allow organizations to build custom models that will only handle a small amount of data.
Advantages of Artificial Intelligence as a Service
In an increasingly automated digital world, there are a multitude of benefits to AlaaS.
Here are just a few of them:
- Reduced cost. AlaaS helps small and medium-sized companies, in particular, become more profitable by minimizing outlay. Profitability increases as companies are able to avoid hiring programmers or investing in expensive machinery. Put differently, they don’t need to build, test, and implement artificial intelligence systems from scratch.
- Ease of use. The vast majority of AlaaS companies offer packaged products that don’t require expertise to implement. Having said that, developers from the business using AlaaS can easily tweak the product if desired.
- Scalability and flexibility. Some businesses will be uncertain as to whether Artificial Intelligence as a Service is right for them. This uncertainty can be alleviated by starting small and then scaling later as knowledge and confidence increase or corporate requirements change. To help facilitate the onboarding of AlaaS, many providers offer their services at a fixed rate. This increases flexibility because customers are free to pay for what they use, and no more.
- Ecosystem growth and integration. The most robust systems are fully integrated, but integration is hindered when artificial intelligence can only be used in a small subset of business operations. Companies such as NVIDIA and Siemens have partnered with AlaaS vendors to overcome incompatible technologies – thereby allowing product teams to increase integration, speed, and efficiency.
How Does AIaaS get monetized?
As the world itself explains, AIaaS gets monetized in the form of subscription/retainer that comprises the management, running and monitoring of the AI/ML Models that are used as the foundation fo the provided service.
Imagine the specific case of a company providing AI models for improving manufacturing processes. The AIaaS company will work on cleaning the data from the customer, plugging that into its AI models to generate reports, monitoring, and workflows for process optimization.
Imagine also the case of a company providing NLG (natural language generation or automatic text generations using the latest language models), which will be running, and operating those models while the customer gets as output generated pages or workflows, paid in the form of retainer.
Part of AI services will also require maintenance, or new experimental projects can be undertaken. In these cases, those can be part of the retainer or charged separately on a pay per consumption basis as MLOps.
- Artificial Intelligence as a Service allows businesses to incorporate AI functionality without the requisite knowledge or experience.
- Artificial Intelligence as a Service can be divided into four distinct categories: cognitive computing APIs, bots and digital assistance, machine learning frameworks, and fully managed machine learning services.
- Artificial Intelligence as a Service offers a number of benefits to customers. AlaaS is a flexible and scalable service that reduces operating costs and is relatively simple to use. As more organizations work toward full integration, the service itself will become more efficient.
- Introduction to AIaaS:
- AIaaS enables organizations to integrate AI functionality without requiring in-depth AI expertise.
- Cloud-based providers like Amazon AWS, Google Cloud, Microsoft Azure, and IBM Cloud serve as the foundation for AIaaS offerings.
- Benefits and Use Cases:
- AIaaS allows businesses to experiment with AI in a low-risk environment without significant upfront investments.
- It falls under the “as-a-service” suite of products, similar to SaaS, PaaS, and IaaS.
- Offers various use cases, including inventory management, manufacturing optimization, and text generation.
- Layers of AIaaS:
- AIaaS is built on top of cloud infrastructure, similar to how SaaS and PaaS are built on IaaS.
- The “as-a-service” models offer solutions to customers without complex on-premise implementations.
- Types of AIaaS:
- Cognitive computing APIs allow developers to incorporate AI through API calls for tasks like computer vision and natural language processing.
- Bots and digital assistance, such as chatbots, enhance customer service and automated interactions.
- Fully-managed machine learning services provide templates and pre-built models, suitable for non-technical organizations.
- Machine learning frameworks allow building custom models for specific tasks.
- Advantages of AIaaS:
- Reduced costs, especially for small and medium-sized companies, by avoiding the need for extensive AI expertise and infrastructure.
- Ease of use with packaged products that can be easily implemented and customized.
- Scalability and flexibility, enabling businesses to start small and expand as needed.
- Ecosystem growth and integration, allowing AI to be seamlessly integrated into various business operations.
- Monetization of AIaaS:
- AIaaS is monetized through subscription/retainer models, encompassing management, operation, and monitoring of AI/ML models.
- AIaaS companies clean and process customer data to generate reports, monitoring, and optimization workflows.
- Maintenance, new projects, and MLOps (Machine Learning Ops) may be part of the subscription or charged separately.
- MLOps includes best practices, workflows, and processes to create, run, and maintain machine learning models for operational processes.
- Key Takeaways:
- AIaaS enables businesses to incorporate AI without extensive expertise.
- It encompasses various categories like cognitive computing APIs, bots, machine learning frameworks, and managed services.
- Benefits include cost reduction, ease of use, scalability, flexibility, and ecosystem integration.
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