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A Practical Guide to Machine Learning: Understand, Differentiate, and Apply


So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.



By Rob Thomas and Jean-François Puget, IBM.

It’s now clear that Machine Learning represents the new frontier in analytics, and is the answer to how companies can capitalize on the data opportunity. Machine Learning was first defined by Arthur Samuel in 1959 as a “Field of study that gives computers the ability to learn without being explicitly programmed.” Said another way, this is the automation of analytics, so that it can be applied at scale. What has been highly manual for decades (think about an analyst combing thousand-line spreadsheets), is becoming automatic (an easy button) through technology. So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.

A relative graphic to explain:

Since the time that Machine Learning was defined and throughout the last decade, the application of Machine Learning was limited by the cost of compute and data acquisition/preparation. In fact, compute and data consumed the entirety of any budget for analytics which left zero investment for the real value driver: algorithms to drive actionable insights. In the last couple years, with cost of compute and data plummeting, machine learning is now available to anyone, for rapid application and exploitation.

 

Adapting at Speed

 
Businesses must constantly adapt to changing conditions: competitors introduce new offerings, consumer habits evolve, and the economic and political environments change, etc. This is not new, but the velocity at which business conditions change is accelerating. This constantly accelerating pace of change places a new burden on technology solutions developed for a business.

Over the years, application developers moved from V-shaped projects with multi-year turnaround, to agile development methodologies (turnaround in months, weeks, and often days). This has enabled businesses to adapt their application and services much more rapidly, from sales forecasting for retailers to product recommendation systems for stock brokers to long-awaited personalized healthcare system.

These scenarios, and others like them, create a unique opportunity for machine learning. Indeed, machine learning was designed to address the fluid nature of these problems.

Firstly, it moves application development from programming to training: instead of writing new code, the application developer trains the same application with new data. This is a fundamental shift in application development, because new, updated applications can be obtained automatically on a weekly, if not daily basis. This shift is at the core of the cognitive era in IT.

Secondly, machine learning enables the automated production of actionable insights where the data is (i.e., where business value is greatest). It is possible to build machine learning systems that learn from each user interaction, or from new data collected by an IoT device. These systems then produce output that takes into account the latest available data. This would not be possible with traditional IT development, even if agile methodologies were used.

 

Getting to the Feedback Loop

 
While most companies get to the point of understanding machine learning, too few are turning this into action. They are either slowed down by concerns over their data assets or they attempt it once and then curtail efforts, claiming that the results were not interesting. These are common concerns and considerations, but they should be recognized as items that are easily surmounted, with the right approach.

First, let’s take data. A common trap is to believe that data is all that is needed for successful machine learning project. Data is essential, but machine learning requires a clear business goal or outcome. Projects that start with little or no data, yet have a clear and measurable business goal are more likely to succeed. The business goal should dictate the collection of relevant data and also guide the development of machine learning models. This approach provides a mechanism for assessing the effectiveness of the models.

The second trap in machine learning projects is to view it as a one-time event. Machine learning, by definition, is a continuous process and projects must be operated with that consideration.

Machine learning projects are often run as follows:

  1. They start with data and a new business goal.
  2. Data is prepared, because it wasn’t collected with the new business goal in mind.
  3. Once prepared, machine learning algorithms are run on the data in order to produce a model.
  4. The model is then evaluated on new, unforeseen, data to see whether it captured something sensible from the data. If it does, then it is deployed in a production environment where it is used to make predictions on new data.

While this typical approach is valuable, it is limited by the fact that the models learn only once. While you may have developed a great model, changing business conditions may make it irrelevant. For instance, assume machine learning is used to detect anomaly in credit card transactions. The model is created using years of past transactions and anomalies are fraudulent transactions. This model can then be deployed in a payment system where it flags anomalies when it detects them. This is effective in the short term, but clever criminals will soon recognize that their scam is detected. They will adapt, and they will find new ways to use stolen credit card information. The model will not detect these new ways because they were not present in the data that was used to produce it. As a result, the model effectiveness will drop.

The cure is to monitor the effectiveness of model predictions by comparing them with actuals. For instance, after some delay, a bank will know which transactions were fraudulent or not. Then it is possible to compare the actual fraudulent transactions with the anomalies detected by the machine learning model. From this comparison one can compute the accuracy of the predictions. One can then monitor this accuracy over time and watch for drops. When a drop happens, then it is time to refresh the machine learning model with more up-to-date data. This is what we call a feedback loop. See here:

Of course, the feedback loop applies to far more than fraud detection. Even in the retail banking sector, we’re seeing machine learning models evolve via feedback loops to:

  • Customize withdrawal limits
  • Optimize tax considerations across portfolios
  • Detect spending patterns
  • Accept or reject mortgages and loans
  • Assess credit limits
  • Retain customers
  • Offer sentiment & news analysis
  • Battle identity theft
  • Automate documentation review
  • Detect risk in financial statements
  • Recommend additional products to customers

With a feedback loop, the system learns continuously by monitoring the effectiveness of predictions and retraining when needed. Monitoring and using the resulting feedback are at the core of machine learning. Just as humans perform a new task, learn from our mistakes, adjust, and act, machine learning is no different.

(To hear about more detailed industry use cases over the next weeks and months, follow the Inside Machine Learning publication here on Medium.)

 

DataFirst: Three Steps to Success

 
Companies that are convinced that machine learning should be a core component of their analytics journey need a tested and repeatable model: a methodology. Our experience working with countless clients has led us to devise a methodology that we call DataFirst. It is a step-by-step approach for machine learning success.

Phase 1: The Data Assessment

The objective is to understand your data assets and verify that all the data needed to meet the business goal for machine learning is available. If not, you can take action at that point, to bring in new sources of data (internal or external), to align with the stated goal.

Phase 2: The Workshop

The purpose of a workshop goal is to ensure alignment on the definition and scope of the machine learning project. We usually cover these topics:

  • Level set on what machine learning can do and cannot do
  • Agree on which data to use.
  • Agree on the metric to be used to evaluate results
  • Explore how the machine learning workflow, especially deployment and feedback loop, would integrate with other IT systems and applications.

Phase 3: The Prototype

The prototype aims to show machine learning value with actual data. It will also be used to assess performance and resources needed to run and operate a production ready machine learning system. When completed, the prototype is often key to secure a decision to develop a production-ready system.

In recent months, we’ve launched five Machine Learning Hubs across the globe where we walk customers through the DataFirst process. To learn more, reach out to us at MLHub@us.ibm.com.

 

Machine Learning is Competitive Advantage

 
Leaders in the Data era will leverage their assets to develop superior machine learning and insight, driven from a dynamic corpus of data. A differentiated approach requires a methodical process and a focus on differentiation with a feedback loop. In the modern business environment, data is no longer an aspect of competitive advantage; it is the basis of competitive advantage.

This story originally appeared on my blog in 2016 and a lot has happened in the industry since then. IBM is moving quickly to advance ML on many fronts, not the least of which is in the area of data science. I encourage you to check out how ML is fueling this burgeoning area of development.

 
Rob Thomas is General Manager, IBM Analytics, and a Vandy and Gator alumnus. He is an avid reader, and the author of The End of Tech Companies and Big Data Revolution.

Jean-François Puget, PhD, is an IBM Distinguished Engineer, working on Machine Learning and Optimization. He is based in Saint Raphael, France.

Original. Reposted with permission.

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