Understanding Artificial Intelligence as a Service (AIaaS) by@ravisharmaonly

Understanding Artificial Intelligence as a Service (AIaaS)

April 12th 2018 14,170 reads
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Ravi Sharma

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 provider platforms which present different forms of Machine Learning as well as AI. These variations can be used by different organizations and this will help them ascertain their need for AI and whether AI works for them or not. There are some Cloud AI service providers which provide specialized hardware required for few AI tasks, such as GPU based processing for intensive workloads etc. Buying such hardware and software can be too costly in the beginning. So AIaaS seems to be a solution for a lot of organizations.

In 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, ForecastThis have 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.

How AIaaS began?

Even though the concept of Artificial Intelligence has been doing rounds since 1960’s, 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) sensors, 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 wide array of algorithms which facilitate solving of specific tasks by computers. This is achieved by doing a general analysis of data.

In the past, companies needed a lot of 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 off the shelf as per your need. AIaaS enables everyone, regardless of how much knowledge they possess, to take benefit of AI. For the developers clean APIs are given, the users get coding skills graphical user interfaces together with detailed instructions in order to ensure data processing pipeline.

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

Given below are few examples of Artificial Intelligence services in the cloud which are available now:

  • 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 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 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 form of analytics cloud service.

Look at the given complicated problems in the transportation sector. Popular shipping companies, like UPS and FedEx desire to find the most efficacious and economical way to transport most packages. The public transportation companies have to recognize city traffic patterns in order to keep the vehicles moving at a rapid pace without building gridlocks. From doing an analysis of how to pack in a maximum number of packets in a delivery van to finding out as well as navigating the fastest routes to deliver those packages, multiple technologies such as the IoT as well as huge data analytics need AI to provide a solution to these knotty problems.

Also, whatever we learn from the commercial use of Artificial Intelligence can be put to the application in day-to-day operations of companies. For instance, we can analyze the traffic patterns in order to determine most convenient delivery.

The principles behind learning how to competently pack in a delivery van can be put to use for purpose of IT optimization and how to best put in order and spread out workloads to lessen the quantity of on-premises servers or more resourcefully make use of cloud resources.

Whenever people think about Artificial Intelligence, they tend to relate the idea to “human-like intelligence” or “general” intelligence. Even though this might be possible in coming times, the present day platforms as well as models are fragmented and have a capability of offering solutions to just domain-specific issues.

So, for the enterprises which have different complicated issues to solve, it necessitates wide array of services from unlike platforms coming together, this is the reason why making AI technology, as well as applications available by means of open sources, is so important for an enterprise.

By leveraging several AI cloud services, companies can come up with solutions to offer the answer to an endless list of multifaceted problems.

About Ravi Sharma

I help businesses to build:

✔ Their better connectivity with their existing customers

✔ Helping them finding new customers

✔ Process improvement with better solutions

✔ Digital branding to attract new markets

✔ A better LIFE!

To find out more about how I can help you, contact me.

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