The Rise of Machine Learning-as-a-Service

Machine learning, artificial intelligence and big data are all buzzwords floating around, seeping their way into all industries

Their uses once seemed far off for companies, but with the introduction of Machine Learning-as-a-Service (MLaaS), data science is being brought to the masses.

Machine learning, a branch of artificial intelligence, is the process of using self-iterating algorithms to analyse massive amounts of data by learning from the information and processing it with minimal supervision. Essentially, machines can learn from themselves through advanced algorithms data scientists create. This technology has implications across all fields, which is why financial institutions, health services, and more are all scrambling to hire skilled data scientists.

Given the demand for machine learning services, MLaaS offerings have recently sprouted up to meet this need. 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. With the advent of cloud computing, these services can be outsourced and are therefore available for use in many industries to provide unique insights or services to their customers. Machine learning is no longer for the big, financially sound companies; now, more businesses can access the benefit of the technology.

Why Machine Learning is Important

Machine learning algorithms can collect and analyse data at a speed no human can. This type of model can forecast trends, create real-time data, and make accurate predictions based on the data. Furthermore, machine learning algorithms learn from mistakes and past outcomes, continually improving predictions.

Businesses can, therefore, make much smarter, data-driven decisions, gaining insights on their company that no human would even think to look for in a data set. From fraud detection to price optimisation, there seems to be no realm machine learning can’t improve.

For organisations that want to take advantage of these benefits and competitive advantages, the best option is MLaaS, which takes the shape of services in the cloud with automatic learning tools. The various MLaaS options can be used solely in the cloud or in a hybrid fashion, depending on each company’s preferences.

The Current State of MLaaS

Machine Learning-as-a-Service is nothing new. Microsoft’s Azure ML, Amazon’s Amazon ML, Google Cloud ML, and IBM’s Watson have been around for some time now. Those platforms, however, are geared towards data scientists and highly technical users. Users can create machine learning models quickly without fully learning the complicated algorithms. Large data teams backed by large, financially sound companies usually use these products to move machine learning products quickly to market.

This model is changing, however, with the introduction of new MLaaS companies that offer small- to medium-sized businesses (SMBs) personalised services. And since a qualified data scientist is hard to come by, more businesses are taking advantage of these options.

The Benefits of MLaaS

Just like Software-as-a-Service, machine learning software is often hosted by the vendor, which means SMBs do not have to worry about in-house capabilities. Many businesses simply do not have the infrastructure to house this amount of data, and they do not have the internal resources to manage the data either. Furthermore, housing this data is extremely costly, and most companies cannot afford to to invest in that. MLaaS companies handle housing and management of the data, taking the burden off the SMB. Many MLaaS organisations offer personalised, scalable technology, where the SMB can pick and choose which capabilities are necessary.

The technology SMBs can then utilise is limitless, making these companies much more competitive in the marketplace. For example, whereas once Google’s Siri and Microsoft’s Cortana dominated the voice assistance space, MLaaS options can allow SMBs to improve this technology for their services as well. Furthermore, with machine learning, companies can take advantage of technology that holds near-natural conversations with people, putting them on a more level playing field with the likes of Google and Amazon.

The Future of MLaaS

Market research shows the MLaaS market is set to have CAGR of 49 % during the forecast period 2017-2023. The report goes on:

The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on.

MLaaS brings these technologies to the masses by offering a way to reap the benefits without fully building out the technology. The MLaaS market will likely continue to grow as more companies realise the potential machine learning has on their business.

About the author

Jennifer Roubaud is the VP of UK and Ireland for Dataiku, the maker of the all-in-one data science software platform Dataiku Data Science Studio (DSS), a unique advanced analytics software solution that enables companies to build and deliver their own data products more efficiently.