Machine learning is coming to the mainframe

CA Technologies recently unveiled new machine learning capabilities as part of a comprehensive strategy for the efficient management of the mainframe production environment.

Don’t blink!  Another major technology revolution is upon you, and it’s getting everyone’s attention: machine learning and advanced analytics come to the mainframe! In fact, CA Technologies recently unveiled new machine learning capabilities. At last, a comprehensive strategy for the efficient management of the mainframe production environment.

“So what?” you might ask?  Machine learning and intelligent mainframe operations are your key to driving MTTR toward zero. With it, you can empower your IT team to leverage predictive analytics and automated remediation to reduce MTTR by fixing problems before they impact the business.

What exactly is Machine Learning?

Machine Learning (ML) has been here for decades, and many of the concepts and algorithms have been around for many years. It is exciting technology that allows processing enormous amounts of data.  And, thanks to today’s performance optimized hardware, this data can be analyzed within an unbelievably short amount of time.

ML itself is an interdisciplinary field that shares many paradigms and concepts with other fields of mathematics and statistics, and can be also viewed as a part of Artificial Intelligence (AI). However, in contrast to AI, ML does not try to imitate an intelligent behavior, but rather focuses on algorithms that can process huge volumes of data and detect patterns that are not obvious or easily deduced by humans. For example:

  • ML algorithms create “models” based on some known data. Models then make data-driven predictions (decisions) on new, unseen data. It means that the model is not a program you would code, but instead is generated logic that can interpret the data and provide some output.
  • The algorithms learn from and make predictions on data. So, once again, “it’s all about the data” – data are crucial for starting and continuing ML.

 

Machine learning applicability

ML can be helpful in the following areas:

  1. Classification. Example: a spam filter could be a good candidate, or any other category or class assignments.
  2. Regression. Like classification, but the output is not a category or class. Example: Temperature forecasts and stock price changes.
  3. Clustering. Often complements data mining, is helpful when you want to find some data patterns. Example: anomaly detection.

It’s all about the data

You can’t just pick up a ML tool, feed it data, and magically receive perfect output. There is no “silver bullet” for today’s Data Scientists (I believe that this will be one of the key roles in the coming millennium). Some basic “block and tackle” is needed that should include:

  • Review the data
  • Extract the valid properties
  • Convert and transform the data (if needed)
  • Select a relevant model
  • Train, validate and deploy.

 

So, where to start?

By now you probably can see I am a ML enthusiast who loves to share my knowledge. I’d like to give you a few points of reference where you can begin your own research and hopefully develop a love for ML as I have. Please consider these:

 

Machine learning keep on learning

What I’ve shared with you in this blog is just the “tip of the iceberg” and I hope it gave you an idea of what ML can provide to you. For now, if you are excited to get started, think about your data and possible use cases. Then learn more about ML from the sources mentioned earlier.

I’d like to keep the conversation going, so if you have any ML tips, tricks or best practices, please share. I’d also like to hear from you if you’re interested in more, deeper, ML discussions and applicability.

Are you attending the IDUG (DB2) Tech Conference (for DBAs) or the separate track for IDUG Data Tech Summit (for Data scientists) in Anaheim (April 30 – May 5)? My CA colleagues (and fellow IBM Champions) and I will be presenting several sessions, including ML on Spark. Also, CA will feature a hands-on lab where you can BYOD (Bring Your Own Device) and test drive the future.  Hope to see you there!

For now, check out this solution brief: how you can reduce operations costs and improve mainframe performance with ML.


Emil has been working in the Prague Technology Center since 2005. He started on resource…

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