Battle for Machine Learning Dominance Intensifies. Is it Worth the Cost?

The increasing heaps of structured and unstructured data in enterprises has fuelled the need for Machine Learning

The world’s largest tech companies are making massive Artifical Intelligence and Machine learning investments. They are electrifying all solutions and applications with machine learning technology.

In 2016, Eric Schmidt, Chairman of Google’s parent company Alphabet, predicted: “machine learning would be the basis for every major IPO within five years”. Closer to this prediction,  machine learning has become the basis of most of the acquisitions in the tech industry.

Battle for Machine learning Leadership Gets Aggressive

The Transparency Market Research (TMR) report observes that the global machine learning as a service(MLaaS) industry is highly competitive in nature.  Leading technology apps and companies are acquiring start-ups to enhance capabilities. Spotify acquired Sonalytic and MightyTV that boasts machine learning capabilities. Sonalytic developed machine learning-based music recommendation technology to help find context-based music for road trips, gym workout, etc.  MightyTV, a TV recommendation app uses machine learning technology that combines genre-base strategies with collaborative filtering based on user ratings for recommendations.

Applications for face recognition, voice recognition, predictive analysis, deep learning, in-app recommendations use machine learning technology. From malware to data security and health, machine learning algorithms offers better information and more patterns on these platforms to enhance user experience.

Among the leading suppliers of MLaaS include Microsoft Corp., IBM corp., Amazon Web Services, Google Inc., BigML Inc., Hewlett Packard Inc., FICO,  and PurePredictive Inc. According to TMR, Amazon, IBM Corp., and Microsoft held more than 73% of the overall market in 2016, due to their continual development and innovation in machine learning techniques.

Big players are merging with small-scale regional players

These players are likely to be increasingly involved in mergers, acquisitions, and partnerships with small-scale regional players in order to expand their reach.

Adhering to the trend, Microsoft acquired Maluuba, a Toronto-based startup that uses deep learning for natural language processing(NLP). The start-up can outperform Google and Facebook. In the preceding year, it made three more acquisitions in the machine learning space, of Wand labs, Swiftkey and Genee. Wand Labs is an NLP platform, whereas  Genee is a startup for AI enabled publishing tool.

This space has undergone a sea change in the past three-four years. Google bought Deep Mind an AI startup in 2014 for $500M.  Simultaneously, Salesforce joined the race with MetaMind and open-source machine learning server PredictionIO. In the first quarter of 2016, Salesforce spent nearly $75M on acquiring three start-ups dedicated to machine learning.

The competition is so intense that Google acquired Kaggle- the world’s largest community(nearly 8lakhs) of data scientists and machine—learning enthusiasts. These data scientists explore, analyze and understand the latest updates in machine learning and data analytics. With this deal, they have access to Google Cloud technology’s powerful infrastructure, scalable training and deployment services. In addition, it provides the ability to store and query large data sets. The increasing heaps of structured and unstructured data in enterprises have fuelled the need for MLaaS. This data needs to be analyzed for future predictions and used further for marketing and other purposes, TMR reports.

MLaaS demand is highest in healthcare: IBM grabs the chunk

Many machine learning start-ups are dedicating maximum efforts to the healthcare industry. The medical industry considers cloud computing and machine learning as a huge boon.

 According to TMR, the demand for MLaaS has been higher in the healthcare and life sciences industry. It will remain so over the next few years, owing to the increasing need to integrate structured as well as unstructured data in this industry.

In 2015, Jasmine Fisher, senior researcher at Microsoft Research in Cambridge, had said that cancer will be a solved problem in ten years, through the cloud computing power that will enable a portfolio of new research capabilities to crack the code. They are using machine learning to help the world’s leading oncologists figure out most effective cancer treatment for their patients.

IBM has been taking giant leaps in gaining health data through acquisitions. It bought healthcare analytics company Truven Health adding “200 million patient data to its data trove of 100 million.

As data is the fuel for machine learning, IBM’s also acquired Phytel, a health management software company; and  Merge Healthcare, a medical imaging company; and Explorys, a Cleveland Clinic spinoff.  With this data, they run complex machine learning algorithms.

Private Cloud leads, But Public Cloud will soon take over

The growing adoption of cloud-based technologies is another important factor behind the significant growth of the MLaaS market worldwide. Mainly because many companies have shifted towards cloud computing and it is easier for them to uptake machine learning services.

The TMR study states that private cloud contributes for the majority of the revenue generated in the global MLaaS market. Enterprises are preferring private cloud-based MLaaS solutions over their public cloud-based counterparts due to data security reasons.

Security versus Infrastructure Cost: Pay-As-You-Go Pricing Model Comes to Rescue

As the location of public cloud servers is at the cloud service provider, enterprises can make huge infrastructure cost savings. Moreover, Public cloud incorporates a Pay-As-You-Go pricing model – making it easy for enterprises to spend and save. Gartner projects that the public cloud market will hit $191 billion by 2020, from 2013’s total of $58 billion. Security parameters in the public cloud are at its maximum. On account of their easy and cost efficient installation, it is anticipated to gain momentum over the forthcoming years. Small- and mid-size enterprises, especially, are adopting the public cloud-based MLaaS solution.

Machine Learning Algorithms Penetrating into Cloud-Based DMP Solutions

Top public cloud vendors like Microsoft Azure, Google,  Amazon Web services, want enterprises to feed large amounts of data into their platforms for machine learning algorithms to learn and acquire knowledge from those data sets.   For this reason, Google’s machine learning start-up competition has a special p**** for the company who implements it on Google Cloud. These vendors are also advertising their public clouds as the ideal test beds of medical research- the field that holds maximum value for MLaaS.

However,  the increasing consumer concerns over data security and a shortage of skilled professionals is limiting the adoption of machine learning.  Despite this, the global market for MLaaS is expected to rise at a phenomenal CAGR of 38.40% during the period from 2017 to 2025 with its opportunity increasing from US$1.07 bn in 2016 to US$19.86 bn by the end of the forecast period, as per TMR.

SAP Chief Innovation Officer, Juergen Mueller envisaged, “One day we will think about machine learning the way we think about electricity. It’s hard to imagine the world without it.” As companies get aggressive to gain a bigger chunk of the ever expanding MLaaS market, that world doesn’t seem far.