What is AI as a Service (AIaaS)March 12, 2022 2022-03-21 12:29
What is AI as a Service (AIaaS)
What is AI as a Service (AIaaS)
- What is AI as a Service (AIaaS)?
AI as a service (AIaaS) refers to AI platforms that allow businesses to implement and scale AI techniques at a fraction of the cost of a full-fledged in-house AI. The term “Service” in AIaaS refers not only to the delivery model – cloud-based software – but also to the level of involvement of the vendor in the process. AIaaS providers provide the entire AI stack as a unified platform, from problem definition to keeping the model on track and expanding to new use cases, in addition to developing the model, deploying the solution in production, and maintaining it in real-world conditions.
AI as a service is a new AI adoption model that has its roots in the recent Software as a Service, or SaaS, wave. As industries across verticals began to shift toward the web as the primary application delivery mechanism, initially for external customer-facing use, but increasingly for internal enterprise delivery, the SaaS model gained traction. During this time, there was an increasing demand for rapid software delivery, including initial deployments as well as feature additions, which could now be deployed onto a server with immediate effect.
With applications rapidly shifting from single on-premises deployments to cloud-based solutions that allow for continuous development, it was only a matter of time before this model was adopted to allow for the deployment of AI solutions.
AIaaS enables businesses to implement customized AI solutions while investing minimally in AI domain expertise. AIaaS vendors understand vertical industries and create sophisticated models to address their specific use cases with remarkable efficiency. Because AIaaS solutions are cloud-based, providers can deliver them as a service that can be accessed, refined, and expanded in ways that were previously impossible.
The AI as a service model has gained traction in the last year, owing to the fact that AI-based solutions can be used by businesses across verticals and use cases. Because AIaaS providers maintain their infrastructure while businesses leverage services, these solutions are cost-effective for businesses willing to invest in AI. The AIaaS model enables businesses that have been unable to embrace AI due to a lack of appropriate off-the-shelf solutions or in-house AI expertise to adopt, deploy, and run effective, customized solutions for their unique use case, data, and business needs — with the shortest possible time to value.
- The Advantages of AI as a Service
AI as a Service allows businesses to implement and run advanced AI solutions at a fraction of the cost of building and maintaining their own model in production. AIaaS solutions also provide greater flexibility, usability, and scalability. In a nutshell, the emerging Artificial Intelligence as a Service model enables organizations to implement highly customized cutting-edge AI solutions while continuing to focus on their core business, rather than diverting valuable resources and attention to new and complex areas of development.
Here are some of the ways these advantages can be noticed when implementing AIaaS solutions in the real world.
- Rapidly develop, deploy, run, and maintain AI-based technologies.
Each AI use case is distinct. Even within the same vertical or operational area, each manufacturer employs unique data to achieve unique goals based on unique business logic. All of these specifications must be translated and built into the model for the AI solution to provide a perfect fit for the data, usability, and business needs. The customized solution is not an end in itself, but rather a means for AI to provide value. ROI can only be generated with a customized solution because standard solutions do not address the specific needs and constraints of the business and thus are ineffective.
- Obtain and maintain robustness and stability.
A solution is required to deal with extreme data scenarios such as noisy, unstructured, or small data sets 24 hours a day, seven days a week. Integrated technologies and AI expertise are critical to achieving the robustness and stability that are the true litmus tests for effective AI.
- Maintain value over time.
While getting started can be difficult, it is only the first half of the battle. The second half of the battle involves maintaining production to ensure that the model does not deviate as data and circumstances change. Maintaining AI in production necessitates data and model version control, updates, optimization of human machine interaction, monitoring of the model’s robustness and generalization, and continuous input noise detection and correlation. Ongoing maintenance can be a difficult and costly aspect of supporting AI solutions.
- Allow for a company-wide AI transformation.
Companies that adopt AI incrementally see more value faster than companies that adopt AI at the organizational level. However, it is critical to maintain consistency in the AI deployment approach in order to avoid the development of a patchwork of soloed use-case-based AI solutions that are incapable of performing holistically.
- What Are the Various Types of AIaaS?
AIaaS solutions come in a variety of flavors. Which type you choose depends on your business goals and what you want to improve. Here are some of the most common types of AI as a service solution:
- Virtual assistants and bots:
The most popular type of AIaaS right now is bots and digital assistance. Chatbots, digital or virtual assistants, and automated email services are examples of well-known AI technology. Natural language processing (NLP) is used by bots and digital assistance tools to learn from human conversations. They are most commonly used in customer service and marketing.
- APIs for cognitive computing
APIs are abbreviated for application programming interfaces. This type of AIaaS solution enables developers to add a specific technology or service to an application they are developing without having to write code from the ground up. NLP, computer vision, knowledge mapping, intelligent searching, translation, and emotion detection are all popular API services.
- Frameworks for machine learning (ML)
Machine learning frameworks are tools that allow developers to create their own AI models. They learn over time by analyzing previously collected customer data. The benefit of ML frameworks is that they do not require large amounts of data to function. This means they can work for smaller businesses that don’t have access to large amounts of data.
- Fully managed machine learning services
Fully-managed machine learning services provide the same functionality as machine learning frameworks but do not require developers to build their own AI model. This type of AIaaS solution, on the other hand, includes pre-built models, custom templates, and code-free interfaces. This kind of AIaaS is ideal for businesses that do not want to invest in development tools.
- What Are AIaaS’s Merit and Demerit
Building in-house AI capabilities will necessitate a significant investment and expertise. It also takes a long time to develop and test AI models before they can be deployed. However, with AIaaS solutions, you can avoid this outlay and the risks that come with it while still utilizing the AI capabilities you require.
- Setup is quick and simple.
AI as a service does not require complicated setup because it is a ready-to-use solution. Instead, you can simply plug it in and immediately gain access to your desired AI capabilities. This has the advantage of eliminating the need to hire a team of data scientists or build complex infrastructure.
- Fees that are clear
When you opt for AI as a service, you only pay for what you get. This means you won’t be paying for AI functions your company doesn’t require, and you’ll only be charged when you’re actively using it.
- Scalable and adaptable
AIaaS allows you to scale up or scale down your artificial intelligence capabilities based on the needs of your business or projects. This adaptability makes it ideal for people just starting out in AI as well as companies that may grow significantly in the future. It also allows you to test what works before making a commitment.
- Decreased security
You will have to share your valuable company data with a third-party vendor in order to use your AIaaS solution. This may raise security and privacy concerns. Data storage, access, and transit must be adequately secured to prevent data from being improperly accessed, shared, or distributed. Some industries may restrict cloud data storage entirely, making AIaaS use difficult.
- Visibility of the black box
While AIaaS solutions provide greater cost transparency, you are only paying for a service rather than access to the process. In other words, you know what goes in and what comes out, but you don’t know anything about the algorithms used or how AI generates the results.
- Reliance on third parties
You are relying on third parties to provide you with the correct information when you need it because you are paying for a service. However, this will become a problem if a software problem causes errors or a delay.
- Long-term and escalating costs
While AI as a service can be extremely cost-effective, fees are ongoing and can quickly spiral as additional capabilities are added. However, while costs may rise, they will most likely only rise in accordance with your business. Furthermore, the insights you gain have the potential to significantly increase profits.
- What Does AIaaS Mean for Companies?
AIaaS, which packages AI capabilities into an accessible and simple-to-use service, has the potential to be a game changer for businesses.
It enables businesses to use their data to solve complex problems and to gain faster, more accurate insights into their customers and markets, allowing them to make better business and marketing decisions. It also enables them to improve customer service and automate and personalize communications, thereby improving the customer experience. Finally, the capabilities provided by AIaaS solutions can assist businesses in increasing profits and gaining a competitive advantage.