Machine Learning as a service: The way ahead for digital transformation

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The phenomenal growth of cloud-based offerings such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS) has resulted into bigger competition in the market, with the new addition being Machine Learning as a Service (MLaaS).

Machine Learning has emerged as one of the fastest evolving technologies today. One of the most critical factors for Machine Learning implementation is to have huge sets of data and to have machine learning (ML) experts or Data scientists who can identify a pattern in data, hiring whom can be difficult and expensive.

Moreover, selecting a machine-learning algorithm is a process of trial and error. It is also a trade-off between specific characteristics of the algorithms, such as speed of training, memory usage, predictive accuracy on new data etc. MLaaS is the answer to both these issues.

Cloud can be an ideal platform for machine learning due to storage of huge data, high computational performance, and low deploying cost. In case of MLaaS, the provider handles the actual computations in their own data centers, with customers not having to install their own softwares or run their own servers.

MLaaS is a set of services offered to companies that give them access to machine learning technologies without contracting a data scientist, allowing them to assess and learn from data by using computers and algorithms to gain deep insights about their data. The services include predictive analytics, deep learning, natural language processing and others.

The major advantage of these services is that they do not require any kind of installation. They can be utilized directly from the cloud and start deploying tools integrated in it. Machine learning is no longer for the big, financially sound companies; rather the companies that want to dip their toes into the machine learning trend without diving right in, can do so through MLaaS.

Google’s Cloud Auto ML is serving the enterprises lacking expertise in data science by providing machine learning or AI as service. Oracle Corporation is also integrating machine learning across all of its services through cloud platform, which will help customers by lowering cost, reducing risk and helping them in making smarter decisions.

Public vs Private Cloud

Many companies have already shifted towards cloud computing and it is easier for them to uptake machine learning services. Private cloud contributes for the majority of the revenue generated in the global MLaaS market. Enterprises prefer private cloud-based MLaaS solutions over their public cloud-based counterparts due to data security reasons.

However, security parameters in the public cloud are at its maximum. Because of their easy and cost-efficient installation, it is expected to gain momentum. Moreover, public cloud incorporates a Pay-As-You-Go pricing model – making it easy for enterprises to spend and save. Small and mid-size enterprises, especially, are adopting the public cloud-based MLaaS solution.

What does the service really offer?

MLaaS can make high-performance machine learning infrastructure more accessible and affordable. Besides, an organization can easily apply machine learning tools to cloud data without moving it. Provision for development tools provided by MLaaS providers can simplify the process of embedding machine learning in applications, while ensuring availability of proven ML technology solutions through the easy and fast creation of ML models.

MLaaS can also enable deployment of machine learning models as web services, along with high scalability and computational performance. Integration with other cloud services of the same provider, such as storage services, is another key benefit of MLaaS.

The technology is enabling business professionals without coding expertise or advanced degrees, to use machine learning. Google has very recently announced AutoML, which it said would “make AI accessible to every business.”

However, vendor lock-in can be a deterrent to the uptake of MLaaS as switching cost to another vendor can be high. Besides this, integrating data from disparate data sources and getting this data into a usable state can be a difficult task, consuming a lot of time.

The India story

With the rapid modernization of its infrastructure and increase in digital connectivity, India is poised to become a hub for AI-enabled businesses. Effective utilization and knowledge creation of AI systems could well propel the country to greater economic efficiency by plugging current productivity gaps.

Experts have clearly called out that adoption of AI/ML technologies would be much faster in developing countries like India in comparison to developed nations like US, Japan, China just because of the magnitude of change these technologies bring.

In the information technology (IT) sector, leading Indian technology firms such as Wipro, Infosys, and TCS, which traditionally provided back-end services, are now providing AI support through softwares developed in-house.

What lies ahead

According to estimates, the MLaaS market is expected to have CAGR of 49 % during the forecast period 2017-2023. Top public cloud vendors like Microsoft Azure, Google, Amazon Web services are trying various strategies to make enterprises feed large amounts of data into their platforms for machine learning algorithms to learn and acquire knowledge from those data sets.

Although MLaaS is still in a nascent stage, it may become a dominant AI/ML platform for enterprises who want to reap various benefits it provides.

With a rapidly growing volume of data, companies and researchers are demanding feasible and affordable ways to extract knowledge from it. MLaaS offers a cost-effective way to engage in the analytics required to succeed in a rapidly evolving environment. Lack of skilled resources in ML technology may encourage organizations to choose MLaaS.

The limitations of MLaaS such as data safety and inadequately pre-trained network as a service may throw some challenges, but services like Google’s AutoML, that actually builds a custom AI model instead of simply providing a customizable pre-processing layer, can address some of these concerns.

Regardless, MLaaS has potential to become a much more sought after service in 2018 and can emerge as a key driver behind ML adoption, primarily because of the easiness it provides to businesses and developers in taking advantage of machine learning capabilities.

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