From Talk to Tangible. A Real-World Guide to Machine Learning.
An Introduction
Despite all the talk about AI and ML, the most successful projects are really not about Machine Learning. The most successful projects are about enhancing business operations and accelerating strategies such as automating highly manual efforts or identifying new revenue sources. In other words, Machine Learning is not the goal but a means to achieving your goals. Success isn’t dependent on identifying the right algorithm but identifying the right business imperative that could best be impacted by Machine Learning.
In this series, I’ll cut-through the hype and go straight to the real-world applications and benefits of Machine Learning. Stick with me to get educated on how to explore, engage with, execute, and eventually enhance ML for your organization.
It’s a Journey
Before diving in, it’s important to acknowledge that Machine Learning is a journey for everyone! Organizations are at different maturity stages in their adoption of Machine Learning- some are learning about it, some are formulating use-cases, some are piloting, and some are in production and seeking to improve their Machine Learning capabilities or adoption.
Below are a couple of steps to get started on your organization’s journey now.
- Why? Most ‘Whys’ are a need to anticipate and drive positive change before their competitors — what’s your why?
• Assess your organization’s maturity level from an analytics perspective — is there a problem to be solved that has broad agreement and urgency? Does your team know enough about how Machine Learning has solved similar problems before? Is the expected Machine Learning solution simple to understand and can it be easily validated?
In the next post of this series, we’ll share why Machine Learning is happening now, the expected disruption caused by Machine Learning, and the common organizational and technical elements that lead to successful Machine Learning initiatives.
