Machine Learning-as-a-Service (MLaaS) exists as the nexus point for some of the most promising technologies and applications of Big Data analytics. The availability of sophisticated Machine Learning algorithms on demand via the Cloud has significant ramifications for:
- Big Data: In many ways, the predictive capabilities of Machine Learning are the only means by which the enterprise can make use of all its data. MLaaS can enable organizations to combine structured data with unstructured, external data to automate analytics processes that would otherwise take too long to parse with Big Data sets.
- Cognitive Computing: As one of the central components in Cognitive Computing, Machine Learning helps organizations take a more cognitive approach to their Data Management by producing applications with cognitive capabilities.
- Data Science: Machine Learning-as-a-Service has the propensity to both reduce the need for scarcely found Data Scientists and substantially assist them in basing models and business objectives on an organization’s data. In either case it eliminates the need to create each individual algorithm.
- Application Development: The capability of MLaaS to accelerate the Data Science process ensures that developers can create better applications more expeditiously to derive near real-time action from analytics.
- Data Modeling: Once initial algorithms are construed, Machine Learning automates the Data Modeling process by producing models on both present and future data to expedite what otherwise would be a time consuming affair.
- Cloud Computing: The speed and convenience of MLaaS are accessed through the Cloud, which reinforces this medium as the architecture of choice for Big Data.
The cumulative effect is that Machine Learning-as-a-Service expands on the possibilities of Big Data analytics while making them more accessible than ever before. James Kobielus of IBM recently indicated that:
“These value points derive from Machine Learning’s core function: enabling analytics algorithms to learn from fresh feeds of data without constant human intervention and without explicit programming.”
Some of the most eminent Cloud service providers (Amazon, Google, Microsoft, IBM) are offering MLaaS either independently or as part of other platforms. Twitter recently underscored the importance of Machine Learning by acquiring Whetlab, a Machine Learning startup. Perhaps the most immediate of the aforementioned ramifications of MLaaS is the fact that it enables developers to readily incorporate Machine Learning into their applications. Most providers deliver MLaaS as an offering used expressly in conjunction with their Clouds, which underpins the need to facilitate Big Data applications off premises. More importantly, the ability of developers to utilize Machine Learning algorithms in their applications reduces the reliance on Data Scientists and the complexity associated with creating these algorithms. Applications involving Machine Learning include any assortment of uses from fraud detection and recommender engines to pattern mining, clustering, and other aspects of e-commerce that hinge on Big Data analytics. MLaaS enables developers to take on more responsibility for Data Modeling and Data Science.
In addition to reducing the need for Data Scientists, Machine Learning-as-a-Service can significantly assist these professionals by increasing the complexity of the underlying analytics algorithms that empower the business. One of the key repercussions of MLaaS is that the increased availability of Machine Learning makes it and Big Data less individual concerns, and more integral to overall processes in Data Management. Without having to create each and every algorithm for each and every application, Data Scientists can tackle more profound aspects of Machine Learning such as Deep Learning and neural networks. In this way, MLaaS becomes as valuable a tool for Data Scientists as it is for organizations without Data Scientists, since it enables the former to expand on the capabilities of this discipline within Data Management.
All of the typical benefits of utilizing the Cloud apply to MLaaS: reduced cost, less infrastructure, increased time to value. Additionally, MLaaS offers the sort of boons that other specialized analytics services (such as Graph Analytics-as-a-Service) provide. Those include a conservation of resources dedicated to hiring specialized personnel and the ability to simply outsource difficult analytics work and its analysis. Certain MLaaS vendors, for instance, will not only construct predictive models and algorithms, but also provide analysis of an organization’s data as part of their services. Although each provider’s process and capabilities differ, most base charges on the amount of data and the length of time service is used—some have specific prices for different Machine Learning functions. Virtually all of them involve APIs to make data machine readable and provide a framework for basic algorithms and models that organizations can tailor to varying degrees. An assortment of visualization tools and programming languages is supported, while there is a minimal reliance on code.
The Point of Big Data: The Internet of Things
Machine Learning and MLaaS are projected to play an integral role in the facilitation of the Internet of Things (IoT). The IoT is arguably the ultimate expression of Big Data. It enables perpetual connectivity and constant streaming of data from any variety of gadgets, from the industrial to the personal. It would be virtually impossible for a team of Data Scientists to continually refine the algorithms and models required for real-time and predictive analytics of the immense quantities of data involved in the IoT. The problem with the IoT that Machine Learning ameliorates is the need to not only account for potentially billions of sensors and their constant streaming, but to analyze them in way that produces timely action. Virtually the only way to do so is to build future models and algorithms from historic and real-time data, which is what Machine Learning does. MLaaS expedites that process.
Natural Language Process Involvement
There are pivotal aspects of both Machine Learning and Cognitive Computing that are predicated on Natural Language Processing (NLP). Although IBM’s Machine Learning APIs are available exclusively through Bluemix and various offerings related to Watson, they help to highlight some of the critical ways in which text analytics via NLP can create a cognitive focus for application building. Some of the more utilitarian Machine Learning services involving NLP include capabilities for relationship extraction and user modeling. The former plies through sentences to identify various points of significance (subjects, actions, places); the latter creates predictions based on text and language analysis about social traits for specific people. Other NLP applications translate different languages and make sense of colloquial expressions. Although these particular features are associated with Watson, additional MLaaS providers have NLP features to enhance their offerings as well.
Neural Networks and Deep Learning
Some of the more sophisticated applications of Machine Learning-as-a-Service involve Machine Learning algorithm approaches known as Neural Networks and Deep Learning. Although service providers might offer the rudiments of these complex algorithms, a more likely scenario for their deployment requires the involvement of Data Scientists and Machine Learning experts. Neural Networks are best understood as an alternative to linear algorithms; these curved, multiple algorithms are named for their similarity to how neurons work in the human brain. Developments in computing power and infrastructure have advanced to the point where the massive volumes of data required to get substantial return on these algorithms are possible. Deep Learning algorithms are types of Neural Network algorithms in which there are substantially more layers and levels of abstraction. Deep Learning algorithm processes are much more complicated than the simulation of human thought processes that Neural Network algorithms provide.
In many ways, Machine Learning functions at the epicenter for a number of different facets of Big Data analytics. Its pivotal role only increases with the availability of MLaaS, which helps to democratize this subset of predictive analytics and enhance the roles of laymen and experts alike. As one of the enablers of the IoT, Machine Learning has a secure place in the future of Big Data. Its capacity to create timely action from analytics makes it essential to Big Data applications. Machine Learning’s ultimate value for Big Data and analytics is alluded to in the aforementioned IBM blog:
“In many ways, MLaaS can be the return-on-investment (ROI) capstone of Big Data initiatives because Machine Learning algorithms can grow to be highly effective at data scales in volume, velocity and variety. Without MLaaS capabilities that can dynamically respond to myriad concurrent data streams in the cloud, the human race risks drowning in its own Big Data.”