“Modern Machine Learning is devoted to deriving value from data, not jamming the airlocks.”

The above quote from Arthur C. Clark’s HAL 9000 vision is about to become a reality, thanks to Big Data enabled Machine Learning (ML). With modern computers exhibiting exceptional performance and storage devices being available within easy budgets, global businesses are increasingly indulging in Machine Learning models to tackle the petabytes of Big Data. In the recent years, Machine Learning solutions have brought Big Data to the mainstream business workflow.

Big Data has signaled a new era of information, where by credible estimates, about 50-75 billion connected devices have entered the daily lives of people. Connected devices distinguish themselves from other types of electronic devices by the sheer, explosive volume of data they produce at every human touch point and interaction.

The uniqueness of Machine Learning is that it delivers systems with trainable capabilities; an ML system is fully prepared to learn from past and current data and then apply that learning for either future predictions or decision making. Thus, the success and effectiveness of ML models depends on the availability of high-speed, high-volume, and high-variety data, and Big Data perfectly fulfills that requirement. The powerful capabilities of a built ML system ensure accurate, timely, and actionable results.

When KDNugget echoed the sentiments of Gartner in Gartner 2015 Hype Cycle: Big Data Is Out Machine Learning Is In, little did they know that both Big Data and Machine Learning together will soon conquer the Data Analytics ecosystem. Gartner’s Special Report suggested that Big Data was not visible on Hype Cycle 2015 after entering a trough of disillusionment in 2014. ML, on the other hand, made a fresh appearance on the charts in 2015, but dwindled into “past the peak” phase later. What this Report indicates is perhaps that both these technologies have been well integrated into business practices and therefore cease to remain as hype.

Machine Learning and Big Data Complement Each Other

International Data Corporation’s annual study  reveals that by 2020, the volume of data on our planet will reach 44 zettabytes, which simply means about ten times the data volumes in 2013.  Currently, global businesses are generating huge volumes of data, much of which remains underutilized in terms of Data Analytics. The traditional Analytics tools and techniques are seriously handicapped to make efficient use of such complex data. For example, Ancestry.com stores around 10 petabytes of records. Given such an explosive growth rate of business data, the modern Data Scientists are faced with the challenge of making the best use of the data. Fortunately for the Data Science community, Machine Learning now provides a great opportunity to tap into the hidden wealth of Big Data.

In pre-Big Data era, Business Analytics was severely limited by data speed, volume, and frequent human intervention for execution. In that scenario, much of the useful data remained untapped. With the emergence of Big Data, Data Scientists started utilizing the science of Machine Learning to extract value from large volumes of data. In case of ML, the more the data, the higher the chances of accurate and insightful solutions.

On the grounds, here are examples of global enterprises and institutions making use of Machine Learning in Big Data Analytics:

• IBM Watson’s open-source, API Library
• Microsoft’s Azure platform
• Google’s Deepmind
• Stanford University and DARPA’s Deepdive
• MIT’s ConceptNet5

As ML professionals continue to experiment with Big Data to tune and enhance their models, here are some challenges facing the global Data Science community.

Challenge 1: Selecting Appropriate Tools for Machine Learning Projects

As global data supply far surpasses petabytes and inches towards zettabytes, the existing ML tools are gradually dropping below the expected capabilities. Thus, one big problem confronting all ML developers is searching and finding the right tools to bring their projects to fruition. Currently, instead of making use of existing open-source technologies, Data Scientists often create their own home-grown tools. This trend has fueled a culture of fragmentation among the Machine Learning development platforms. With the lack of a single development framework, businesses are left with many available solutions that must be compared on the merits of usability, performance, and adaptable algorithms. However, the truth is that “one size does not fit all,” hence ML developers are advised to invest considerable time and effort if identifying their needs and matching tools.

The paper from Journal of Big Data titled A Survey of Open Source Tools for Machine Learning with Big Data in the Hadoop ecosystem provides a comprehensive review of the available, open-source development frameworks and tools for Machine Learning projects. This paper is suitable for Data Scientists, researchers, engineers, and system developers already well versed in Machine Learning concepts and workflows.

The evaluation criteria to apply while selecting appropriate frameworks has been properly explained in the article, and specific comparison tables of data-processing engines, ML libraries, and frameworks have also been provided, with specific discussions on the relative advantages and disadvantages of each. The article offers helpful overviews of the Hadoop ecosystem, MapReduce, Spark, Mahout, MLlib, and more.

Challenge 2: Unlocking the Power of Big Data enabled Machine Learning

As global businesses develop an increasing dependence on AI-centric strategies for competitive edge, Big Data driven Machine Learning solutions will gain more importance among the business community. Machine Learning Set to Unlock the Power of Big Data  explains the friendly tool that Facebook offers to connect users with new friends, the definitive virtual guide that Netflix uses to suggest a new TV series to online audience, or the recommendation tool that Amazon uses to recommend a book to a customer—are all live examples of ML solutions—that can make reliable predictions based on Big Data-driven data troves.

Although initially perceived as hype, Big Data has continually proven it’s potential. A Gartner survey found that more than 75% of companies are currently investing or planning to invest in Big Data initiatives over the next two years. The survey further notes that this renewed interest signals a staggering investment of $242 billion in Big Data projects.

In 2016, the persistent buzz among the global business community is that Machine Learning will play a far greater role in Big Data Analytics by delivering highly accurate, time-bound, and actionable, insights to the businesses. Thus, this year may be regarded as the “Dawn of Enlightenment for Machine Learning.” ML is capable of combining the immense power of real-time data with automated process models, thus perfectly primed to handle the complex, disparate, and high volume Big Data.

Case study: Attribution Models in Retail Businesses   

In Future of Analytics Software: Big Data & Machine Learning, examples of retail businesses using attribution models have been explained clearly. The article suggests the actual purchase journey of an online customer is far more complicated than it is assumed in traditional Analytics practices. The numerous interrelated factors leading to online conversions are frequently overlooked. Customers are generally led to a purchase decision through a complicated web of interactions—web browsing, content sharing, Ad watching, newsletter signups, social media discussions etc—before making a decision. This complicated customer behavior is frequently overlooked by current Analytics systems thus leading to incorrect predictions.

Big Data now enables Machine Learning models to continuously collect, adapt, and analyze customer behavior data over a long period of time for improved results.

In today’s retail business context, the concept of “assisted conversions” is gaining much importance as online buyers are engaging in a multi-channel environment to personalize their consumer journey. However, the present Analytics models are limited in their understanding of how the different channels actually influence the buyers. Thus, the general hope is that Big Data enabled Machine Learning models will provide powerful algorithms to help buyers make informed decisions. Truly effective attribution models can vastly enhance the buyer engagement with Brands.

Also review the DATAVERSITY® article  Improving Big Data Analytics with Machine Learning as a Service.

Challenge 3: Strengthening R&D Practices with Big Data Enabled Machine Learning

In November 2015, ReadWrite pointed out that Machine Learning can easily uncover the weak spots in current R&D practices through hidden and inter-related patterns available in huge volumes of data. For example, DARPA is working on a system that is assumed to discover and plug security loopholes in software systems. Many other innovative R&D solutions vouch to the fact that sound business models are derived from vast and variable data utilization.

This article makes a convincing case for Machine Learning projects that depend heavily on Big Data. In the end, the readers are likely to believe that without Big Data, ML models cannot deliver “value in real time.”

Machine Learning-driven Analytics have previously questioned established beliefs about innovation and research in businesses. More and more, ML models are disrupting the old and outdated R&D practices to pave the way for data-powered, research processes. In such a research scenario, the existing data plays a crucial role in teaching the model to learn and adapt to changing trends and patterns. Thereafter, the research process itself is guided by the data.

For example, a company called Local Motors is extensively using 3D printing technology in auto manufacturing R&D. In the research phase, the company prints out a fully functional vehicle design in about fewer than 48 hours. With accurate data indicators, this research approach can aid a small-sized manufacturer to quickly address the custom needs of customers.

Challenge 4: Machine Learning Models in Manufacturing Practice

While certain Machine Learning techniques have been around for decades, global manufacturing hubs still depend largely on the human decision-making process on a day-to-day basis. Now, Big Data technology can change all that. With this fascinating technology, now shop-floor managers can apply ML techniques to extract highly accurate insights from petabytes of operational data for improved performance and decision making.

A TCS white paper, Using Big Data for Machine Learning Analytics in Manufacturing explores how Machine Learning algorithms, in conjunction with Big Data technologies, can help manufacturers bring about operational and business transformation.

Challenge 5: Machine Learning has to Power Big Data in the Cloud

 In Cloud-based, Big Data applications, “workload categorization” has a large impact on the efficiency and reliability of the systems. Cloud-Based Machine Learning Tools for Enhanced Big Data Applications offers proven ML tools and techniques for enhanced Big Data applications, although the technical nature of the article content may be well beyond the comprehension of general business readers.

 Final challenge: Machine Learning Must Bring Big Data on the Grounds

As the Information Age article has rightly pointed out, today’s Machine Learning professionals must address two issues:

• They must appropriately prepare models to interact with the ever-increasing volume, velocity and variety of data. These models must also be tuned to deliver accurate predictions and actionable insights.
• The ML models must be refined to such a degree so that they are fully equipped to discover data trends and patterns that even the sharpest human minds could miss!

The future Machine Learning models should be developed with the mainstream business users in mind and not just the Data Scientists. Future achievement will lie in brining the Big Data enabled Machine Learning solutions to the mainstream business users. Machine Learning, to be truly useful, must gradually scale new heights beyond the ivory tower of Data Science, and transform Business Analytics to an easily applicable technology in businesses of all shapes and sizes. Dataconomy’s Understanding Big Data: Machine Learning explores the possibilities of powerful algorithms to reach beyond the realm of robotics and enter our everyday lives.