This shift has been made possible because of the cloud computing revolution, particularly the massive growth of public cloud services provided by enterprise companies such as Amazon, Microsoft and Google, among others.
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These enterprises have put an enormous emphasis on the “as a service” business model, which allows outside companies to pick and choose necessary microservices provided by the enterprises.
Machine learning as a service, or MLaaS, is defined as services from cloud computing companies that provide machine learning tools in a subscription model in the forms of big data analytics, APIs, NLP, and more.
Infrastructure as a service (IaaS) and cloud platform as a service (PaaS) are the two most commonly used service offerings; however, in the coming year businesses will rapidly begin to adopt machine learning as a service (MLaaS) into their technology stacks for a number of reasons, the main one being the need to progress company-wide digital transformation.
By adopting artificial intelligence software and services, businesses can enhance product capabilities, better interact with customers, streamline business operations, and create predictive and precise business strategies.
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Developers can build quickly and efficiently with MLaaS offerings, because they have access to pre-built algorithms and models that would take them extensive resources to build otherwise. The developers who have the knowledge and skills to build machine learning models are few and far between, and are very expensive, so the ease and speed of setup coupled with the monetary benefits will be a major draw for business implementing AI in 2018.
Data is the driver behind machine learning, and because these huge companies produce and have access to so much data, they are able to build and train their own machine learning models in house. This allows them to offer it to outside companies as MLaaS, the same way that since they have more datacenter space than smaller companies they can provide IaaS. Generally, smaller companies do not have access to as much data to create powerful AI models; however, they do have valuable data that can be fed to pre-trained machine learning algorithms to create business-critical outcomes or actionable insights.
There are a number of MLaaS offerings for businesses to choose from, including natural language processing (NLP), computer vision, AI platforms and other machine learning APIs. Amazon, Google, Microsoft and IBM all offer different services for these machine learning functionalities. Additionally, these different types of AI can have unique impacts on many aspects of digital transformation.
Amazon was the first major enterprise company to offer IaaS and PaaS, and it has taken that business model and applied it to all the technologies needed to scale a business. Amazon Web Services (AWS) now has a large catalog of microservices that companies can purchase to create their own digital platforms.
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The concept of digital platforms and microservices are extensive, but AWS, along with Microsoft’s Azure and Google’s Cloud Platform, offer everything from cloud computing, storage and database management, to augmented and virtual reality, business productivity applications, and tools for the internet of things. These microservices are largely API-based so they make for a quick deployment, which is largely the appeal of these products. It is challenging and time consuming to build a development environment if you are trying to rapidly scale or build a product.
The last point is something that Amazon as a company came to realize early on when it was trying to build new in-house applications. According to Andy Jassy, AAWS’ CEO, around the year 2000, before Amazon was the dominant force it is today, it hoped to build an external development platform called Merchant.com to assist other merchants (such as Target) in building their own online shopping sites atop Amazon’s e-commerce engine. However, in order to build the external development environment, Amazon needed to clean up its internal environment.
As many rapidly growing companies do, Amazon did not build its in-house solutions to scale, so it began to reorganize its development environment into a series of APIs that could then be used by development teams across the company. As teams built out easy-to-understand documentation around the APIs, it became simple to share internally, and increase the speed and ease of developing new applications for Amazon, which helped Amazon grow faster and more efficiently. This concept is the same as it is today for AWS’ microservices that can be purchased by third-party companies.
Similarly, Amazon found that it was struggling with siloed infrastructure throughout the development team, which made projects lengthier than they needed to be. “When Jassy, who was Amazon CEO Jeff Bezos’ chief of staff at the time, dug into the problem, he found a running complaint. The executive team expected a project to take three months, but it was taking three months just to build the database, compute or storage component. Everyone was building their own resources for an individual project, with no thought to scale or reuse.”
To solve this issue, Amazon sought to build a singular internal infrastructure service that all development teams could have access to so there would be no need to build their own every single time.
This infrastructure necessity forced Amazon into building fast-scaling, reliable data centers that didn’t cost too much money to maintain. As it grew and compiled more and more data centers, Amazon began to realize that outside companies could run its applications on top of Amazon’s infrastructure. Some three years later, in August 2006, it released Amazon Elastic Compute Cloud, the first IaaS to hit the market. It took some time for public cloud infrastructure to make its mark in the modern business world, but now the likes of Microsoft, Google and IBM are all still playing catch up, some 12 years later, to gain their own chunk of market share.
It just so happens that these cloud providers are working equally as hard at being at the forefront of AI advancements, and have built machine learning models that they are providing as microservices to outside businesses. Similarly to the way that the other cloud services have grown, and continue to grow rapidly, businesses will greatly lean into MLaaS in 2018, and these enterprise services will be the reason why. There is an opportunity out there to grab market share, similarly to the way that Amazon was able to take hold of the IaaS market, and Amazon, Microsoft, Google,and IBM will fight, scrape and claw for it. In coming years, it will be necessary to include MLaaS as a chunk of the cloud services revenue.
Any time there are a few big companies in a space it appears ripe for disruption, but enterprises are poised to dominate the MLaaS space for a variety of reasons, the first being big data. Simply put, they have it. It is getting easier to access open data sets (which are often open-sourced by the enterprise companies), but these corporations have access to exponentially more data than small or mid-sized businesses. Because they have data they have been able to build machine learning algorithms and train them with said data. This is an advantage that is almost insurmountable for small competitors and startups.
Another major advantage is competitive salaries the likes of Amazon, Microsoft, Google and IBM can offer AI developers, of which there are not many. Lean startups cannot afford to pay even close to a similar salary, and offering a stake in ownership is no longer enough. According to a New York Times piece from October 2017, large tech companies have put such an emphasis on AI that they are willing to pay beyond top dollar. “Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them,” the article read.
Now some might pass up those salaries to build something on their own or join a startup where they can have more control, but those unguaranteed common stock shares are difficult to compare to those shares of the tech giants. “Well-known names in the A.I. field have received compensation in salary and shares in a company’s stock that total single- or double-digit millions over a four- or five-year period,” read the Times’ piece. “And at some point they renew or negotiate a new contract, much like a professional athlete.”
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The ability to grab talent is so important because there isn’t much of it. There is a major lack of people who are knowledgeable and skilled enough to build AI applications. Per the New York Times article, “In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research,” according to Element AI, an independent lab in Montreal, per the New York Times article.
These jobs are relatively new, so while there are many data science and machine learning courses being offered to students, it still takes years to receive the education needed to develop AI, so the talent gap will continue for some time. Since enterprise companies have the resources to attract the talent, they are able to build out their machine learning services, which will only increase the need for other businesses who can’t afford those employees to utilize MLaaS.
Also, many businesses already take advantage of public cloud providers, so adding one more microservice from the catalog is not too much of a hassle. If a business is already storing its data in an AWS or Azure public cloud, it is easy to adopt an MLaaS solution from those vendors. They can work with a business’ data, which is stored on their infrastructure, and help train their machine learning service to benefit the business. Not only will it be a quick deployment, but most likely inexpensive, another draw of microservices in general. This MLaaS approach will save the business time, energy and resources (which they do not have enough of), to help modernize a business with AI.
Enterprises have already released a number of machine learning services for use by outside businesses. These MLaaS offerings consist of AI platforms for custom algorithm building, natural language processing, speech recognition, computer vision and other miscellaneous machine learning models. Below are MLaaS offerings from AWS, Microsoft Azure and Google Cloud Platform.
Artificial intelligence software platforms allow users to build and train machine and deep learning models and applications. These solutions are similar to cloud platforms that help you build applications, in the sense that they often utilize drag-and-drop functionality for easy construction of algorithms and models. Users can pump data through these solutions to best train their models to perform the tasks they need. The following MLaaS offerings are AI platforms.
Natural language processing (NLP) is a form of AI that allows applications to interact with human language. NLP has made major strides in recent years due to the rapid advancement of deep learning, particularly with the utilization of artificial deep neural networks. The applications of NLP include machine translation, grammar parsing, sentiment analysis and part-of-speech tagging, among other uses. NLP is the primary technology behind tools like chatbots. The following MLaaS offerings are NLP solutions.
Speech recognition software models allow applications to understand spoken language and convert conversations into text. Most often, developers combine speech recognition with natural language understanding and intent analysis to determine what a user wants. The most familiar example of this technology in action is with Apple’s Siri, Google’s Home and Amazon’s Alexa; however, the following MLaaS speech recognition tools can be used for a variety of purposes:
Computer vision is the deep learning technology that allows applications to recognize and understand images and videos. These solutions can analyze visual content to identify and classify specific objects, people and streaming video. Computer vision algorithms can help companies such as YouTube ensure that there is not inappropriate content being uploaded onto their sites without requiring a human view every video. The following MLaaS offering provide computer vision
Amazon, Microsoft and Google provide a number of other MLaaS microservices. Here are a few of note:
TIP: Find the highest-rated machine learning software on the market today!
MLaaS will be a driver behind AI adoption in 2018, because it makes it simpler for businesses and developers to take advantage of machine learning capabilities. It will fuel the rise of embedded AI in business software applications, and it will allow organizations to use data in new ways that would be otherwise impossible without hiring a highly skilled AI developer.
The lack of machine learning talent in general will be a reason why businesses choose machine learning services from the likes of AWS, Microsoft Azure, and Google Cloud Platform. With these microservices, it is easy to set up and run machine learning algorithms that enhance business processes and operations, customer interactions and overall business strategy. Companies will most likely begin using the services based on other digital platform offerings that they already use for the likes of cloud computing and IaaS.
Regardless, MLaaS will become a much more sought after service in 2018.
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Rob is a research principal focused on enterprise technology vendors and their continuous battle for market share in the age of digital transformation. Rob's work digs into competitive trends for enterprise giants, such as Amazon, Microsoft, Oracle, and IBM, among others. In addition, he highlights acquisitions, innovative product releases, and unique differentiators between enterprise vendors. He has been with G2 since 2015, and has shaped the direction of G2’s report and research offerings. While the enterprise is professional passion, in his free time Rob enjoys watching as many films as possible and even dabbles in some amateur screenwriting. His coverage areas include enterprise technology and strategy.
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