Our Articles

A Monthly Article from our Speakers

Current Article of the month

Articles of 2019

Articles of 2018

Articles of 2017

Articles of 2016

Articles of 2015

Articles of 2014

Articles of 2013

Articles of 2012

Articles of 2011

Articles of 2010

Articles of 2009

Articles of 2008

Articles of 2007

Articles of 2006

Articles of 2005

Articles of 2004

Articles of 2003

Articles of 2002

Frank GrecoMachine Learning for the Enterprise

by Frank Greco

October 2019

 

The world is full of patterns. As a matter of fact, it is a large composite of many different patterns. Looking for these regularities gives us a good idea on how to anticipate future events for our advantage.

Pattern recognition is useful in many aspects of life.

Music is a collection of patterns. For example, most popular music in general follows a pattern of verses and choruses. Jazz musicians learn their craft by studying a particular repeating pattern of chords. Blues musicians similarly have a different and repeating pattern of chords. By recognizing these well-known conventions or patterns, a collection of musicians in these genres can predict future chords and successfully play together to create music. In competitive sports, recognizing your opponent’s tendencies or patterns can often provide the tactics for success. A Formula 1 car driver may take advantage of the tendencies of another driver who takes very wide turns and cut him off at the next opportunity. A tennis player who notices how an opponent favors their forehand may hit the ball to their backhand to score a point. Detecting these proclivities or patterns allows these players to succeed in sports. In cooking, the best chefs often don’t rely on precise measurements or split-second timing when creating world-class cuisine.

An experienced cook may recognize the typical visual cues when a sauce or gravy should be simmering and not boiling despite what oven temperature a recipe dictates. Recognizing visual or taste patterns are key attributes of successful chefs. Similarly, pattern recognition is very important for business. Like music, sports, cooking and certainly other aspects of our lives, recognizing patterns can help organizations predict and anticipate future events for business advantage. The core of Machine Learning (ML) is all about recognizing patterns in your data, anticipating the likely behavior of similar data and taking steps to maximize business benefit based on those predictions.

BUSINESS PREDICTIONS
Accurate prediction is critical for practically all enterprises. Without a degree of confidence in business forecasting, organizations would have a difficult time delivering successful products and services in a cost-effective manner. ML provides the capability to offer deep predictive and prescriptive decision-making intelligence. This type of enterprise data analysis is vital to all businesses. Over the past several decades, many enterprises in a wide variety of industry sectors have had to rely on data analysis for predictions. We have all heard of terms such as business intelligence, data mining, big data, predictive analytics, etc. All of these tools and techniques look at historic corporate data and help to make educated predictions about the future to increase revenue or profit for the organization.

PREDICTIONS USING MACHINE LEARNING
Machine Learning is a dramatically different approach to business forecasting than these previous tools. But Machine Learning, or “ML”, is definitely not a new concept. It has its origins in artificial intelligence (AI) from many decades ago. The first AI conference was in 1956 at Dartmouth College in the US. This was even before punch cards were invented! Duplicating human intelligence using a machine to simulate the neural network of our brains was a lofty and very exciting goal. However there just were not enough computing resources nor sufficiently sophisticated algorithms to bring this idea to fruition years ago. AI and its subset ML saw minor successes in the late 1980’s and 1990’s but overall despite the exciting possibilities, there was very limited progress. But about 10 years ago, there were some breakthroughs in new, scalable pattern-recognition algorithms using new types of artificial neural networks (ANN). And with the advent of cloud computing that can deliver enormous computing resources on-demand along with the vast amounts of data available, the power of Machine Learning was magnified exponentially. This new form of ML is popularly referred to as “Deep Learning”. This new more powerful form of ML is delivering accurate predictions at unprecedented levels. Whether you call it Deep Learning or continue to label it Machine Learning, this type of analysis and prediction is a huge business and technical trend for many enterprises and will continue in the foreseeable future.

OVERALL PROCESS
From a high-level, a Machine Learning system is quite simple. At first, large quantities of “clean”, valid data are analyzed looking for statistical patterns. These patterns are codified into a model and tested thoroughly. Once the enterprise is satisfied the model can deliver results consistently, it is put into production and makes predictions based on new input. And since we live in a dynamic world of changing data and changing environments, the production models and data are continually tweaked by data scientists to reflect unbiased results. ML REQUIRES

HIGH-QUALITY DATA
It is important to ensure the ML system is reading large datasets of “clean”, high-quality data. As with data mining and other traditional enterprise analytics systems, it is critical to have accurate and complete data that truly represents the business. The more data that is available, the more accurate the predictions are. And since large enterprises have proportionally more data than smaller organizations, large enterprises are perfectly suited to reaping the most benefit from ML systems. Deriving collections of comprehensive rules from this growing mass of data collected from sales, user support, sentiment analysis, website traffic, IoT asset tracking, microservices monitoring, network activity, et al, becomes quickly unmanageable. Instead of complex rules systems, statistical methods must be employed to detect patterns.

ENTERPRISE USE CASES FOR ML
When deciding where to use ML in the enterprise, there are several typical characteristics of systems that could potentially take advantage of Machine Learning. A very repetitive system that requires decisions to be made based on past data would be an obvious target area. Here are some areas that potentially could use ML solutions:

  • Sales enablement
  • Customer support
  • Back office expense tracking
  • Predictive maintenance
  • Logistics
  • Language translation
  • Credit risk
  • Insurance pricing
  • Loyalty programs
  • Network intrusion
  • Fraud

There are many other possible use cases where ML can be used. Some use cases are still to be discovered as the depth of problems ML can solve increases significantly along with innovations in learning algorithms and scalable infrastructures. We are at the start of a new, exciting era in predictive and prescriptive enterprise analytics with Machine Learning. It is a massive change in the computing industry and a fundamentally different type of software that uses modern statistics to solve problems without explicit programming. But it requires holistic organizational management since there are many issues concerning ethics, data privacy, bias, interpretability, and team training.

MACHINE LEARNING FOR THE ENTERPRISE - OCT 28-29, 2019 The “Machine Learning for The Enterprise” international conference is focused on the promises, challenges and best practices of applying Machine Learning (ML) in large organizations for business benefit. Large internet companies such as Google, Amazon, Netflix and Facebook have deployed sophisticated machine learning systems for years. However other organizations are looking to deploy the same machine learning techniques throughout their IT systems to benefit their businesses. This conference will focus on defining this innovative technology, describe specific real-word use cases of machine learning in detail, explain how your staff can gain this expertise along with a collection of best practices, and highlight a view of the machine learning offerings now available.