Artificial Intelligence as a Service – AI OFF THE SHELF
In recent years, technology giants like Amazon, Google, Microsoft, and IBM (along with a hosting company) have all started to provide Artificial Intelligence as a service (AIaaS). These services, in essence, offer a broad range of AI algorithms accessible to the public. Some examples are algorithms used to classify, regression, or Deep Learning – a modern learning system based upon Artificial Deep Neural Networks. With increasing frequency, businesses are beginning to adopt AlaaS and other cloud-based services. Having a clear understanding of the best way to use it implemented into your business can be the difference between an enormous cost-saving opportunity and a huge headache.
Involvement of AI
Companies used to spend many hours creating their own AI programs and doing this at a high cost. Since creating an AI infrastructure and the development of AI algorithms oneself is not easy, AIaaS delivers a working solution quickly and efficiently, saving time and cash. AI off the shelf, as it’s often known, is able to achieve this through already set up infrastructure and pre-trained algorithms that reduce the time needed to develop and also the number of resources needed all over the world to accomplish difficult tasks.
Cloud hosting providers have been offering IaaS (Infrastructure as a Service) as well as SaaS (Software as a Service) for a long time. AIaaS is also built on the previous offerings. The concept has been utilized in AI. Apart from reducing time to develop and expenses. Additionally, it reduces the risk of investment and improves flexibility in strategic planning. However, companies must also take into account the drawbacks of AlaaS. They are dependent on a service provider as well as the ability to connect to data at a significant speed. There is also the possibility of a lower level of data security and standardization, which puts limitations on the development of new technologies.
Levels of AI
Alongside the benefits in comparison to disadvantages essential to recognize two different levels in AI: high and low-level AI. The high-level AI solves difficult but ultimately standardized problems. One example of high-level AI is software for face recognition. Because the user interface is straightforward – put an image into the program and then wait for an answer. Even non-AI experts can utilize advanced AI without difficulty.
Low-level AI, however, it is designed to handle various tasks with different needs. Examples include logistic regression, which can be utilized for churn prediction or to detect fraud. The proper use of low-level AI requires expertise in modelling training and data processing and optimizing parameters and their evaluation. The lengthy processing pipeline implies an increased chance of making a mistake during the various problem-solving phases. Consequently, it isn’t easy to put low-level AI into use without AI experts.
With the cost of AI falling and the growing capabilities of AI allowing a more diverse range of companies (many of them not necessarily tech-oriented) to use these two forms of AIaaS, understanding the essentials to keep them running is crucial.
Make the Right Choice for the AI
In the beginning, it is crucial to select the appropriate solution for our company. This isn’t easy due to the fact that AlaaS providers don’t reveal their algorithms’ implementations. The only thing known is the API for an algorithm in most cases. A purchase that is not informed is not a guarantee with regard to AI. As with all software, companies are better off testing the product thoroughly before purchasing it.
In low-level AI, the majority of clients are stuck in creating the right processing pipeline. There are numerous intricate processes involved in this process implemented in different ways by the different service providers. This is why it is advised that companies compare the service to self-coded implementation before accepting anything. Test. Compare. Repeat.
This is vital, as AI algorithms are ultimately the only software that could be unstable. One way to avoid this is to insert our code, which some service provider’s permit. This is a good option, but only if the firm has skilled teams aware of what they want to achieve by making changes to specific code.
Conclusion
When utilized correctly, Alaas is an amazing device that allows nearly all businesses to dramatically increase their capabilities by using AI to use, with an affordable cost in terms of time, equipment, and personnel. The variety of services offered can be as valuable as a hindrance. Research is essential for obtaining the correct AI service and using it to serve the correct goals. Consulting with various service providers is essential, particularly for the latest technology, where there are a few glitches, always something to prepare for. When we do that, an entirely new world of possibilities will open.