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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 hello@mantralabsglobal.com

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Why Netflix Broke Itself: Was It Success Rewritten Through Platform Engineering?

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Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform. 

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Source: getport.io

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization:  Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

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