In the last three years, cloud-based services have dominated the data market in one or the other way. Three important cloud services including software as a service, platform as a service and infrastructure as a service have reached the zenith of their popularity. We are now aiming at machine learning as a service which has a market potential of 2.2 billion US dollars in the Indian context itself. This number is slated to touch about 30 billion dollars by the end of the decade. This is where the need for machine learning training becomes even more significant than ever before. Given the commercial potential of this technology, it is highly likely that a pay-as-you-go model would be adopted by various businesses who want to experiment with machine learning as a service in the future.
In this article, we would examine the prospects of machine learning as a service from multidimensional aspects.
A global service
When we look at machine learning as a service, we find that a number of microservices can be classified under its domain. For instance, various types of microservices like the Google cloud platform and Amazon Web Services come under the domain of machine learning as a service. In addition to this, computer vision, natural language processing, face recognition, image classification and even other deep learning techniques are part and parcel of the service.
As a global service, machine learning makes use of in-house machine learning teams as well as remote experts from around the globe who work on tasks such as data processing, data mining and data visualization. In addition to this, model evaluation and model training is also done as per the requirements of the client. In this way, various types of machine learning services including a broad set of algorithms are offered at a minimum price without the concern of infrastructure constraints.
Positive drivers for adoption of MLaaS
The ongoing slowdown showed us the need for the adoption of machine learning as a service in multiple domains because it may become difficult in future to set up heavy infrastructure and machinery for machine learning computations. While on one hand, we see the growing operation of the internet of things and the spread of automation technology, we also see the growth of machine learning solutions on the other hand.
This points to the need for widespread adoption of machine learning as a service so that the entire process of data modelling eliminates the need for physical and labour-intensive model selection. This will not only have the advantage of solving complex problems with machine learning algorithms but will also decrease the cost of the computational processes drastically.
Throwing caution to the winds
Although we cannot deny the advantages and benefits that accrue due to machine learning as a service, it is also important to understand some challenges that are related to it.
- The first important challenge is related to data security. The users of machine learning as a service fear that critical and sensitive data of the company could be compromised and this may cause the company losses worth millions of dollars.
- The second important challenge is related to data privacy. The clients who use machine learning as a service often worry about compliance with data protection rules that are prevalent in their respective territories.
- It needs to be noted at this point in time that privacy-preserving deep learning technology has been introduced to cater to the above challenge.
- This is a direct indication that the power of federated machine learning is being harnessed to overcome privacy issues and take machine learning as a service to the next level of its adoption.
The wide gamut of applications of MLaaS
The application domain of machine learning as a service is very large. From both the academic as well as industrial points of view, machine learning as a service holds special significance. While the service finds prominent application in digital marketing and advertising industries, it has wide application in other sectors where natural language processing and speech recognition is used.
It is also handy for those sectors that deal in sentiment analysis and are concerned with image classification and computer vision. In the financial sector, machine learning as a service is especially important for fraud detection and securing financial transactions. It is also important in those application domains where predictive analytics and customer analytics are required.
An example of these sectors includes the e-commerce industry, retail, logistics and supply chain management industry. Furthermore, machine learning as a service also has prospective applications in sectors such as marketplace training, stock market, media, entertainment, education as well as healthcare.
Prospects for digital marketing services
Machine learning as a service is extremely important when it comes to the domain of digital marketing. This is because of its useful applications like network management as well as predictive maintenance which are extremely important when it comes to digital marketing services.
In the year 2021, machine learning as a service contributed to the growth of the digital marketing industry by more than 33% in the first quarter itself. This points out to the bright prospects that machine learning as a service has for running digital advertising campaigns, customer targeting as well as brand positioning. With the help of this service, it would be easy to manage the digital platforms of companies.
In addition to this, companies would also be able to leverage the power of data for effective decision-making in their business process. Furthermore, machine learning as a service would also help in the assessment of the performance of a business and comparison with other competitive players in the market.
The bottom line
While machine learning as a service has already impacted and disrupted the markets in Asia Pacific, it is highly likely that this technology will leave an impact in the Indian context as well. As such, it is high time to get acquainted with machine learning as a service including other supplementary technologies at an early stage. This would help the Indian businesses to keep pace with the level of automation that is being witnessed globally in the digital marketing industry.
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