17/03/2020 0 Comment

Functioning of Artificial Intelligence as a Service (AIaaS).

Functioning of Artificial Intelligence as a Service (AISaaS).

“Artificial Intelligence as a Service (AIaaS) is basically third-party offering of artificial intelligence outsourcing. So, people get to take advantage AI without spending too much money, investing in the same and at a much lower level of risk involved.”


There are several AI providers which present different forms of Machine Learning as well as AI services. The permutations and combinations of these can be used by different organizations and this will help them establish their need for AI and whether AI is required for them or not. There are some Cloud AI service providers which provide specialized hardware required for few AI-intensive tasks, such as GPU based processing for intensive workloads, etc. Buying such hardware and software can be too costly in the beginning. So Artifical Intelligence as a Service seems to be a solution for a lot of organizations.

In the past few years, several huge IT firms such as Google (Cloud Platform), Amazon (Web Services), Microsoft (Azure) and IBM (Developer Cloud) and some startups such as BigML, Dataiku, Forecast. This has begun to provide Artificial Intelligence as a Service (AIaaS).

This is done to trim down entry costs which other companies have to pay if they wish to use AI.

Inception of AISaaS.

Even though the concept of Artificial Intelligence has been introduced in 1960s, progress in graphics processing units as well as networking, together with a need for big data, has put the same into the focus of several companies.

Due to a sudden increase in data from various applications as well as the Internet of Things (IoT) sensor feedbacks, and a requirement for real-time decision making, Artificial Intelligence is fast becoming a main prerequisite and differentiator for several cloud providers.

AI consists of a wide array of algorithms that facilitate the resolution of specific tasks by computers. This is achieved by doing a general analysis of data.

In the past, companies needed a lot of resources, money, as well as time to build up infrastructure and technical, know-how for AI applications.

Now, AIaaS has minimized the development time. So, basically, you get AI as per your need. Artificial Intelligence as a Service enables everyone, regardless of how much knowledge they possess, to reap benefits of AI. For the developers clean APIs are provided, the users get coding skills GUIs together with detailed instructions in order to ensure the data processing pipeline.

The ease, as well as self- marketing of service providers, imply that everyone can apply AI algorithms without any problem.

Popular Use cases of Artificial Intelligence as a Service :

  • Amazon’s in-house Artificial Intelligence expertise, for instance, predictive analytics, is available on AWS i.e. Amazon Web Services by means of Machine Learning Service. Amazon is also coming up with open-source software DSSTNE- Deep Scalable Sparse Tensor Network Engine. This fuels the customer recommendation capabilities of Amazon, such as suggesting the kind of books which you may want to read or movies which you will enjoy watching, etc.
  • Google Cloud Platform presents a wide range of home-grown Artificial Intelligence capabilities like speech recognition, translation, predictive analytics, and image content identification. Furthermore, Google also presents its TensorFlow recommendation software library, akin to DSSTNE of Amazon, via an Open Source Apache license. Off late, Google came up with Springboard, which enables enterprise customers to leverage Google’s Artificial Intelligence-based search interface to rapidly surface information from within Google products group. In addition to providing the platform, Google is able to influence its other products to enhance its Artificial Intelligence. For instance, the more pictures which any Android user clicks of cats are uploaded to Google, the better the model Google has for identifying the cats.
  • Microsoft presently offers its Distributed Machine Learning Toolkit to enable users to run multiple as well as varied machine-learning applications at the same time, for instance analyzing images and making use of Microsoft’s Computer Vision and language comprehension.
  • Watson Developer Cloud by IBM helps developers to fit in Watson intelligence in apps which they use and offers its Watson Artificial Intelligence engine in the form of analytics cloud service.

Let’s consider the complicated problems in the transportation sector. Popular shipping companies, like UPS and FedEx desire to find the most reliable and economical way to transport their packages. The logistics companies have to know city traffic patterns in order to keep the vehicles moving at a rapid pace. From analyzing how to transport the maximum payload in a vehicle to determine the fastest routes to commute. Multiple technologies like IoT and huge data analytics need AI to provide a solution to these tricky problems.


Also Read: Importance of building user data pipelines for businesses and How it could help them grow?

And whatever we learn from the commercial use of Artificial Intelligence as a Service could help in the application’s day-to-day operations of companies. For instance, we can analyze traffic patterns in order to determine the most convenient route.

The data could be helpful in IT optimization and to spread out workloads to lessen the quantity of on-premises servers. And to more resourcefully make use of cloud resources.

Future of Artificial Intelligence as a Service?

Whenever people think about Artificial Intelligence, they tend to relate the idea to “human-like intelligence” or “general” intelligence. Though probable in the future, the present-day platforms are fragmented and have the capability to resolve only domain-specific issues.

So, for enterprises with complex use cases, it becomes necessary to merge services from, unlike platforms. This is the reason why making AI technology available by means of open sources, is so important for an enterprise.

With the help of AI cloud services, companies can build solutions to offer the answer to an endless list of complex problems. We at AppVoir leverage such technologies to create cutting edge marketplace products for hyperlocal businesses.


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