As you can imagine, we speak with a lot of clients and potential customers that are very interested in machine learning. I remember the first time I was exposed to the concept, what an incredible step in technology! My mind was racing with the possibilities, and frankly, still is.
The Time Series Data Historian & Machine Learning
I'm often asked what type of machine learning platform Canary offers as an integrated solution. And, recently, I've seen OSIsoft and other data historians really start to press machine learning on their platforms. I think this is a mistake. The role of a data historian, should be to aggregate large collections of data as efficiently, and completely, as possible. Then, make access to that data extremely flexible, this includes in both connectivity as well as the structure of the data itself.
Should Machine Learning Be Part of the Historian?
We have met incredibly intelligent men and women working for forward-thinking companies like SparkCognition, Mnubo, and Seeq, all of which are in the machine learning and predictive analytic spaces. Frankly, they are just going to be better at it than we are. We are experts on data collection. They are experts in data science and modeling. Canary continues to focus on collecting and storing data so that you can partner with companies like these to transform your process and reduce downtime!
10 Things To Know About Machine Learning
A colleague emailed me the Quora Q&A below and I thought it would be a fitting piece to share. I particularly love the focus on how valuable good data is and the requirements of having a lot of it. Seems to fit rather nicely with Canary's focus on providing years of unaltered sensor data. Enjoy the article and don't worry, SkyNet is still not active....
Question: What Should Everyone Know About Machine Learning?
1. Machine learning means learning from data
2. Machine learning is about data and algorithms, but mostly data
3. Unless you have a lot of data, you should stick to simple models
4. Machine learning can only be as good as the data you use to train it
5. Machine learning only works if your training data is representative
6. Most of the hard work for machine learning is data transformation
7. Deep learning is a revolutionary advance, but it isn’t a magic bullet
8. Machine learning systems are highly vulnerable to operator error
9. Machine learning can inadvertently create a self-fulfilling prophecy
10. AI is not going to become self-aware, rise up, and destroy humanity
"What should everyone know about machine learning?" originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.