Machine Learning System

                            Machine Learning System

MLS You’ve been working hard to get the right strategy, systems and processes in place for your big data initiatives. You have really good people that understand how to analyze data, great data management processes, the best systems for data collection and analysis and you’ve started seeing some excellent results.

Although, by all accounts, everyone in your organization is happy with the big data initiatives underway, it still feels like something is missing from your big data system and analysis portfolio. That ‘missing piece’ might just be machine learning

In this article, I’m going to introduce the concept of machine learning and why it’s important to you and your big data initiatives. In future articles, I’ll dive into examples of companies using machine learning as well as a bit more details of how your organization might be able to integrate machine learning into all aspects of your business.

Machine learning can have many different definitions depending on the level of technical depth you are looking at it from. For the purposes of this article, we’ll define machine learning as a an approach that automates the building of models to analyze data.

Machine learning is a quick and effective way to build models that can continuously learn from new data. Additionally, machine learning can be thought of as a ‘force multiplier’, meaning that with the right algorithms, systems and processes, you can build hundreds (or thousands) of models per week compared to the one or two models that a good data analyst can build in that same time period.

In addition to being a force multiplier, machine learning also makes it easy to scale your modeling and learning capabilities. Ask a data analyst to build a model on a data-set today and then ask them to revisit that model in two months after adding an additional ten terabytes of data to the data-set and they’ll pull their hair out. WIth machine learning, your data analytics team can build models, and then let those models continuously learn and adapt to whatever new  data is generated and/or collected.

When I speak to clients about big data, I always include plenty of information about machine learning.  I generally argue that if they are going to generate and collect enormous amounts of data, they’ll want the best systems, processes and people focused on analyzing that data. Included in that ‘best of’ is machine learning.  Machine learning gives you an enormous advantage with your data as it lets you do so much more with what you have.

As I mentioned, in coming posts I’ll be digging into the topic of machine learning with real-world examples, stories of usage within organizations and some thoughts around how to build a culture of ‘deep learning’ within your company.