Machine learning holds the promise of not only structuring vast amounts of data but also to create true business intelligence
The sheer volume and unstructured nature of the data generated by billions of connected devices and systems presents significant challenges for those in search of turning this data into insight. For many, machine learning holds the promise of not only structuring this vast amount of data but also to create true business intelligence that can be monetized and leveraged to guide decisions.
In the past, it wasn’t possible or practical to implement machine learning at such a large scale for a variety of reasons. Recently, three major advances have enabled more organizations to take advantage of machine learning to enhance business intelligence:
1) Bigger data (and more importantly, better labeled data)
2) Better hardware throughout datacenters and high performance computing clusters
3) Smarter algorithms that can take advantage of data at this scale and learn from it
Machine learning, generally speaking, refers to a class of algorithms that learn from data, uncover insights, and predict behavior without being explicitly programmed. Machine learning algorithms vary greatly depending on the goal of the enterprise and can include various algorithms targeting classification or anomaly detection, clustering of information, time series prediction such as video and speech and even state-action learning and decision making through the use of reinforcement learning. Ensembling, or combining various types of algorithms, is also common as researchers continue to push the state of the art and attempt to solve new problems. The machine learning arena moves very fast and algorithmic innovation is happening at a blistering pace.
With machine learning, enterprises can generate predictive models in order to accurately make predictions based on data from large, diverse, and dynamic sources such as text and metadata, speech, videos, and sensor information. Machine learning enables the scale, speed, and accuracy needed to uncover never-before identified insights. The promise of accurate, actionable, and predictive models will drive it to play a larger and larger role in business intelligence as data continues to get more and more unmanageable by humans. This enhanced intelligence provides utility in myriad ways across many industries including Health Sciences for medical imaging, financial services for fraud detection, and cloud service providers and social media platforms for services like powering automated “personal assistants,” image detection, and measuring sentiment and trends. There really is no end to the applicability of machine learning.
Banks, as an example, are applying machine learning algorithms to predict the likelihood of mortgage defaults and risk profiles. By retrospectively analyzing historical mortgages and labeling them as either acceptable or in default, a lender could leverage a trailing data set to build a more reliable analytical model that delivers direct and measurable value well into the future. By crafting models like this that learn from historical experiences, banks can more accurately represent mortgage risk, thereby reducing defaults and improving loan profitability rates.
In order to efficiently develop and deploy machine learning algorithms at scale, enterprises can leverage powerful processors like the Intel® Xeon® processor E7 family to deliver real-time analytics services, and open up new data-driven business opportunities. Organizations can also turn to highly parallel Intel® Xeon Phi™ processors to enable dramatic performance gains for highly data parallel algorithms such as the training of Deep Neural Networks. By maintaining a single source code between Intel Xeon processors and Intel Xeon Phi processors, organizations can optimize once for parallelism but maximize performance across architectures.
Of course, taking advantage of the latest hardware parallelism requires updating of the underlying software and general code modernization. This new level of parallelism enables applications to run a given data set in less time, run multiple data sets in a fixed amount of time, or run large-scale data sets that were previously prohibitive. By optimizing code running on Intel Xeon processors we can therefore deliver significantly higher performance for core machine learning functions such as clustering, collaborative filtering, logistic regression, support vector machine training, and deep learning model training and inference resulting in high levels of architectural, cost, and energy efficiencies.
Advances in high performance computing (HPC) and big data infrastructure combined with the computing capabilities of cloud infrastructure are fueling a new era of machine learning, and enabling enterprises to discover valuable insights that can improve their bottom line and customer offerings.
This blog originally appeared on InfoWorld.com.