True Machine Learning is finally here for all to Leverage
In this short post I want to hit 3 simple points.
- Why has true machine learning been so difficult to provide to the masses?
- Why is machine learning not simply statistical models?
- What type of organization has the power to bring machine learning to the masses?
The simple reason it took so long to bring machine learning from the theoretical to the everyday is that it is hard. No scratch that it is really – really hard to do at scale and price point where every organization can leverage the innate power of machine learning.
Think about it, you have this layer of intelligence over all your machine data that is constantly on the watch for unusual behavior and anomalies. It is constantly learning your unique environment to make sure it is your machine learning and not a simple best practice or signature. Lastly, you now have the power – and better yet the opportunity – to leverage millions upon millions of data points you never used before.
Machine learning is a hot topic today because it is extremely valuable, unique and helps organizations leverage information they have never used before. However, this is where the reality of step one meets the road in step two of our story. True machine learning takes time because it has to perform a series of steps that cannot be short cut.
The mythical “easy button” of machine learning just isn’t practical. If you’re looking for machine learning and someone offers you a solution that produces results in 2 hours, you have a tool that is leveraging signatures or rules but not machine learning.
Once installed, a true machine learning solution will immediately start baselining your environment. However, people need to remember that baselining an environment takes time and patience because true machine learning looks at everything.
Think about like this:
- Week one is when the solution is learning fast.
- Week two is when the solution starts to refine what it has learned.
- Week three you are rocking and rolling as the solution has now “Learned” your environment.
The final point is about why it takes a special community of people to pull off true machine learning. There are lots of factors that need to come together to tackle a unique, powerful and very valuable asset of machine learning at a scale and price point that empowers all organizations equally. The first is that the organization behind the solution or platform has to be proven to think different and not settle for the easy way out like taking sample sizes; they have to be fully committed to the true challenge and opportunity of machine learning. Secondly, the organization has to have a platform behind this challenge that is both powerful and flexible enough to consume all your data because missing any of it should not be acceptable or tolerated if at all possible. Lastly, and this is important and missed by many, the community behind the vendor has to be large, constantly growing and wildly passionate because no single organization on its own can bring all the power, flexibility and speed of machine learning to the masses in a time frame that should be acceptable to all of us.
In closing, machine learning is finally here for the masses.
The Future is Now!!!