Trending Now: Machine Learning Has Arrived
Just a few years ago we were still wondering, “Is machine learning the real deal?” If you aren’t already keenly aware, you should know that machine learning has arrived!
The Year’s Hottest Topic
During this past year machine learning has been on everyone’s lips. From Big Data to mainframe operations, machine learning emerged as a key theme at nearly every conference Syncsort attended:
- Truth and Consequences: Strata NYC Focuses on Promise and Potential Pitfalls of AI and Machine Learning
- Strata + Hadoop World 2017 Recap: Machine Learning, Data Lakes and the Cloud
- My “Aha!” Moments as a Splunk .conf “FTR”
- What Trended at IDUG 2017? Machine Learning, AI, DB2 & Data Analytics!
- SHARE Providence Recap: IBM z14, ITOA, ITSI, SIEM & Machine Learning
The Experts Weigh In
Machine learning is not only impacting the tech world, but also helping to improve various business processes. A number of influencers we’ve spoke with over the last 18 months have had a thing or two to say about how machine learning is affecting their area of expertise.
- Michael Schmidt of Nutonian on the Future of Machine Intelligence
- Founder of Altify on the Future of Artificial Intelligence in Sales and Account Planning
- Databricks’ Damji: Spark + Hadoop = Artificial Intelligence = Science Fiction Becoming Reality
- Splunk’s Andi Mann on IT Service Intelligence, ITOA and AIOps
- EMA’s John Myers on Big Data and Machine Learning for the C-Suite
- IBM’s Holden Karau on Hadoop, ETL, Machine Learning and the Future of Spark
- SoftServe’s Robert Corace on Why Digital Transformation is Critical
The Impacts of Machine Learning
Until recently, only very large organizations had the data management capabilities to leverage machine learning effectively. This is no longer the case. New tools and technologies are enabling companies of all sizes to begin experimenting with machine learning.
Machine learning and artificial intelligence are reshaping the technology world. But it is only as effective as the data that drives it. In other words, if you want to implement effective machine learning, also you need to pay attention to data quality.
More and more practical applications of this technology are starting to emerge. One example is using machine learning to fight plagiarism.
Machine Learning Hits the Mainframe
In March, mainframe expert Alan Radding told us, “The mainframe is emerging as a cognitive machine, and IBM is only making its cognitive capabilities available on premises for the z System. Any other platform has to access IBM’s cognitive capabilities in the cloud.”
So what does machine learning on the mainframe look like? Read our eBook Mainframe Meets Machine Learning to understand how advances in machine learning have started and will continue to strengthen mainframe security and power the automation of mainframe operations.
In our follow up eBook Mainframe and Machine Learning for IT Service Intelligence, we review at how an ITSI solution with machine learning capabilities can provide a comprehensive view of your organization’s service delivery, allowing you to effectively set SLAs, identify potential problems, and plan for changes in the IT environment.