Machine Learning is using modeling to give you data-driven recommendations.
These recommendations are game-changers for companies. They often turn weeks or months of work into a few minutes. However, only if the company has a solid base in business intelligence, predictive, and prescriptive analytics. Without this base, ML is simply a really expensive automated business intelligence.
When done right, Machine Learning is a computer algorithm that improves automatically through experience. A programmer builds a mathematical model based on “sample data”. Then the model predicts outcomes based on what it learned in the sample data. It improves as it uses more data to become “Smarter”. It can be used in a wide variety of applications, such as email filtering, the connection of two disparate systems, or computer vision.
In addition, when we implement Machine Learning, we also make sure the long term support of that solution is in place. Machine Learning is rarely a “set it and forget it” style solution. Being able to adapt the solution to fit the changing environment and/or data sets is crucial to its success long term.
Side Note: There is also a lot of confusion as to the difference between Machine Learning and Artificial Intelligence. If ML is data-driven recommendations, then AI is automated decisions. Without the knowledge of the difference, you can spend mountains of money (and all of your political capital) with no real measurable difference.
At CAN, we understand why there is confusion. Really well-executed machine learning can seem like artificial intelligence. There is a time and place for both, but they need to be executed in the right project scope to be worth the effort.
Want to find out what kind of solution your company needs and not just chase a buzzword? Contact us today to find out for sure.