Machine Learning as a Service (MLaaS) and its business implications in 2019

In this special guest feature, Rob Light, Research Principal at G2 Crowd, believes that the technology behind MLaaS will become prominent in 2019 with the help of the cloud giants, media and consulting partnership opportunities. Rob focuses on artificial intelligence, big data, and analytics categories at G2 Crowd. In his free time, he enjoys watching as many films as possible and even dabbles in some amateur screenwriting.

As artificial intelligence (AI) becomes more applicable to business, the buzz behind the term only grows — and for good reason. AI, more accurately referred to as machine learning, opens up seemingly infinite use cases to better automate business tasks and assist humans in making data-driven decisions.

To really take advantage of machine learning, businesses will need to do one of two things: Invest a lot of resources (money) in data scientists or developers with a background in machine learning, or utilize machine learning as a service (MLaaS) offerings

The latter option can be much more cost-effective for a business that may not have the luxury of hiring ultra-skilled (read: expensive) employees. MLaaS products are microservices sold mainly by large cloud computing vendors such as Amazon Web Services, Microsoft Azure and Google Cloud Platform. 

These cloud infrastructure vendors have the luxury of being able to develop machine learning algorithms in-house because they can hire those highly skilled data scientists. In addition, these companies have access to enormous data sets that are necessary to train machine learning algorithms. Similarly to how Amazon built its own cloud infrastructure for internal use and then decided to make it a platform available to other businesses, these companies have built machine learning tools for their own business applications and decided to sell the services externally. These solutions include pre-built natural language processing (NLP), computer vision and general machine learning algorithms

With so many companies adopting or migrating to a specific cloud infrastructure vendor to build their business, it is easy for companies to purchase MLaaS offering from their cloud infrastructure provider. 

The business implications of MLaaS and its impact on AI trends in 2019 will be focused around two key areas: awareness and implementation

MLaaS will be on every C suite’s radar

In 2019, MLaaS will become mainstream. This year the onus will shift from business leaders needing to seek out information on machine learning to MLaaS vendors promoting their offerings on a larger scale. 

Tech companies will do more to educate business users about the applications and impacts of AI, creating a shift in thought around the technology. No longer will it simply seem like a futuristic wonder tool; companies will break down AI to be more accessible and applicable to day-to-day operations.

A major form of MLaaS promotion will revolve around earnings reports. In every earnings report from Amazon, Microsoft or Google, they reference their cloud computing departments and highlight how quickly they are growing, how much money they are making, and the incredible profit they see from these divisions. In 2019, this paradigm will evolve into not just cloud computing revenue reporting, but also informing investors on how much revenue they are generating from their MLaaS offerings.

As AI becomes more widely adopted, reporting market share and revenue numbers for MLaaS offerings will become a key selling point for these cloud giants. News outlets will begin to emphasize these numbers just as they do the cloud numbers and it will spur their stocks on the same way that cloud computing has over the past five years.

Whether they refer to these machine learning products as MLaaS is yet to be seen, but in one way or another they will reference the services available and how they can be instrumental for true digital transformation. This added level of awareness will not only benefit the cloud behemoths, but it will also encourage businesses to take the leap of faith and implement MLaaS products.

Soon it will not be a battle for cloud market share between AWS, Microsoft, Google and IBM. It will be a battle for AI market share.

Increased implementation will lead to thriving partner practices

Purchasing a machine learning service from a cloud provider is just the first step in utilizing AI. Once you have decided to adopt a natural language processing (NLP) or computer vision solution, you still need to train the service or algorithm to provide proper outputs. With a lack of data scientists in the workforce, as well as a lack of resources to hire those that are available, implementation and consulting partners will thrive thanks to their understanding AI and MLaaS. 

Consultants can be expensive, but in comparison to hiring a data scientist, it could still be a fiscally responsible decision, especially for small and mid-sized businesses. If these partner practices can help to implement and train MLaaS products quickly and efficiently, then they are a wise choice compared to hiring a data scientist. Additionally, data scientists may not be proficient in all vendor solutions, so finding those that have specializations in Google’s MLaaS offerings compared to Microsoft’s will be impactful.

New consulting firms will open up that specialize in training AWS, Microsoft or Google’s machine learning services and helping businesses implement them into daily processes. Many of the large consulting firms like Accenture and Deloitte have already ventured into these spaces and have pre-existing partnerships with those vendors, but small shops will open up at a rapid pace in 2019.

Opportunity abounds for existing consulting firms, particularly those that specialize in data or analytics. Those that have a knowledge of how to prepare and organize data will have a leg up, because that is really the first step in utilizing MLaaS. Machine learning is only as good as the data available and the transition into training algorithms will be a natural evolution.

Those big consulting firms have already starting acquiring some more specialized, smaller practices. In July, 2018 Accenture announced it had acquired Real Time Analytics Platform to expand its AI capabilities. The press release mentions that, “Real Time Analytics Platform leverages AI in the form of machine learning, neural networks and natural language processing (NLP) to analyze every stage of the software testing life cycle, enabling users to make data-driven decisions that reduce defects, optimize test case execution and enhance the power of human decision-making in this process.”

In September 2018, Deloitte bought Magnetic Media’s artificial intelligence platform to specifically bolster AI specialization in the marketing and advertising space. These strategic acquisitions will only continue and become more specific over time. Successful AWS or Google MLaaS practices will be highly sought after, the same way that strong Workday or Salesforce implementation partners are gobbled up by the big players

One way or another, MLaaS will break through in 2019

Despite great efforts, the term MLaaS may not become a household name the way PaaS (platform as a service) or IaaS (infrastructure as a service) have become standard tech terminology. However, the technology behind MLaaS will become prominent in 2019 with the help of the cloud giants, media and consulting partnership opportunities.

Businesses will become more aware of the abundant opportunities these solutions present and implementations will expand, so much so that they may become the reason businesses choose specific cloud computing providers. While that may seem optimistic, it is also a very rational next step in achieving true digital transformation.

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