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This article is about Machine Learning
What is Machine Learning as a Service?
By NIIT Editorial
Published on 04/12/2021
6 minutes
The gradual development of Cloud computing has seen the introduction of varying business models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). On-cloud hosting tends to generate a staggeringly high amount of data of untapped potential. Enabling this data stream to create future-forward insights has given rise to Machine Learning as a Service (MLaaS).
What is Machine Learning as a Service?
The process of lending out machine learning tools and services over the cloud is referred to as Machine Learning as a Service. The service model benefits those who otherwise lack the financial throughput to invest heavily in hiring machine learning experts and onboarding the requisite hardware and software. The upkeep of ML tools such as APIs, data visualisation, deep learning, and natural language processing is handled by the vendor. So are the secondary issues concerning data processing, and predictive analytics.
How does MLaaS Work?
In offering machine learning as a service, vendors take the resource-intensive responsibility of caring out data-heavy computations such as facial recognition, training data sets, and performing deep learning. In advanced stages, machine learning algorithms are left to identify patterns from the data set. As per convention, MLaaS vendors manage the overall computations be it hardware or software. MLaaS stack is an end-to-end AI-stack that has the single-handed ability to run operations for mobile apps, and industrial automation to name a couple of platforms.
With a core team of machine learning experts, MLaaS vendors perform operations such as pattern recognition, statistical computing, and pattern recognition. Leading vendors for MLaaS include the likes of Amazon, Microsoft, and Internation Business Machines (IBM).
Applications of MLaaS
Cross-industrial fields are making use of MLaaS. Its predictive analytics capabilities are used for risk management, fraud detection by financial institutions. Especially during the pandemic, world governments have turned to a multi-pronged, fast-paced installation for facial recognition cameras powered by machine learning. Predictive analytics on MLaaS services is also used for high-end warehouse management, managing retargeting campaigns in digital marketing, and now also healthcare services to recognize early-stage life-threatening ailments.
Benefits of MLaaS
As mentioned earlier, for companies barely latching onto irregular cash streams and dreaming of making it big someday find MLaaS a sizeable, investable, and affordable alternative for data-intensive projects. As known to recruiters, hiring a full-time machine learning expert on top of costly infrastructural demands is an undertaking bootstrapping companies look to avoid.
Vendors such as Microsoft who offer machine learning as a service already have facilities that can scale operations on the go, without delay. And because the service is being meted out at scale, it becomes much affordable.
MLaaS Market Overview
In 2020, the MLaaS market was valued at US$1 billion but the same is expected to grow at a common annual growth rate of 43% to be valued at US$848 billion by 2026. This pace of growth will be multiplied thanks to the corresponding mainstream adoption of IoT devices and Artificial Intelligence.
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