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The Next Big Thing for Big Tech: AI as a Service

4 minutes, 9 seconds read

The biggest challenge with AI practitioners (so far) is to find a considerable volume of relevant data to feed machine learning algorithms. And nobody ever thought that this problem would be resolved in the blink of an eye. 

With huge data repositories, the crowned tech giants —  Amazon, Google, Microsoft, Apple, IBM, Salesforce, SAP, Oracle, Alibaba and Baidu have become the AI leaders of today. Their next venture into AI as a Service (AIaaS), adequately powered by Data as a Service is, yet again, prone to disrupt Digital. 

How will AI as a Service impact businesses?

Organizations may have centuries-old data with them, and they might even invest in digitizing all historic data to generate volume. But, is this data a good fodder for machine learning models? Certainly not. Consumers today are way different from yesterday. What the world needs is real-time data. And who has it? The aforementioned AI leaders, who not only made efforts to collect data but also made arrangements to organize them and use them wherever, whenever. 

Today, Google Home has over half a billion users; meaning — there’s no scarcity of data. With this, Google cloud offers a range of AIaaS products like AI Hub — a repository of plug-and-play AI components; AI building blocks — to make developers utilize structured data into their applications; and an AI platform — a development environment to let data scientists and ML developers quickly take projects from ideation to deployment. 

The point is, the quest for quality data to train ML models is nearly over. The hunt for Machine Learning experts is seeing an end. Because with Google Cloud AutoML developers with limited ML expertise will be able to train their specific ML models. Similarly, Amazon SageMaker provides Managed Spot Training, which can reduce ML models’ training cost by 90%. This drastic cost reduction will encourage businesses to adopt AI at a larger scale; thus opening new avenues for innovations.

Is AIaaS different from MLaaS (Machine Learning as a Service)?

MLaaS is a set of services that offer ready-made Machine Learning tools. Organizations can utilize this as a part of their working needs. The popular MLaaS services available today are (mostly from Amazon, Google, Microsoft, and IBM)-

1. Natural language processing

2. Speech recognition

3. Computer vision

4. Video and image analysis

While ML corresponds to making machines learn by themselves, AI focuses on both the acquisition and application of information. AI is the process of simulation of natural intelligence to solve complex problems. AIaaS, thus, broadens the scope of MLaaS by enabling machines with cognitive capabilities.

We’re rapidly moving beyond the algorithms that were designed to deliver experiences based on predefined rules. “AI… is a group of algorithms that can modify its algorithms to create new algorithms in response to learned inputs…” (Kaya Ismail, CMSWire)

How will AI as a Service disrupt digital products and experiences?

  • With the commercialization of AI, most of the digital products will be equipped with AI.
  • The time-to-market for AI and ML-based products will reduce drastically.
  • AI-enabled products comply with connected data ecosystems, which enables effective organization and utilization of huge volumes of data.
  • AIaaS will deliver multi-layer insights to humans at a moment’s notice. 
  • It will smartly integrate different technologies (like AR) on-need basis.
  • Making sense of regional language data will be no more challenging.
  • Delivering intuitive experiences will become much simpler. For instance, the Google Translate app automatically takes input from the user’s device language settings and applies augmented reality experience to scanned images. 
  • It will connect the dots — IoT, Driverless cars, drones, hyperloop, and even space technologies.

[Related: The State of AI in Insurance 2020]

Getting the edge over operations for the next era of tech

Cloud is changing the AI marketplace and serverless computing is making it possible for developers to quickly get AI applications up and running. Also, the prime enabler of AI as a Service business is information services. The biggest change that serverless computing has brought is — it has eliminated the need to scale physical database hardware. For instance, Amazon Aurora extends the performance and availability of commercial-grade databases at 1/10th of the cost. To mention, Netflix moved its database to AWS to leverage the benefits of serverless computing. Another example of distributed infrastructure for data is that of Microsoft Azure Data Lake. It dynamically allocates or deallocates resources, enabling a pay-per-use model. 

While business benefits from AI as a Service are immense, the competition among AI Leaders is not less. Tech giants pour billions of dollars in AI research to shape the business of the future. What we see today is the outcome of decades of hardship and the quest to get the best minds to execute their AI strategy. 

Read the story – The Big Five of Tech are winning more often with AI — The AI race so far.

We are helping leading Insurers like Aditya Birla Health Insurance, Religare, DHFL Pramerica, and many more in their AI initiatives. Please feel free to talk to us for your AI strategy roadmap and implementation. Drop us a line at


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