Machine Learning – The majority of software development teams believe they don’t test well. They understand that the effect of quality defects is substantial, and they invest heavily in quality assurance, but they still aren’t getting the results they want. This is not due to a lack of talent or effort — the technology supporting software testing is simply not effective. The industry has been underserved.
Information Technology (IT) has always relied on monitoring to provide an understanding of how systems perform over time. The basics has always been to collect a series of metrics and to build an algorithm that shows how these metrics are related, and then to show when a system is either reaching the limits of its capacity or is likely to break.
By now most of us understand that, in our current era, artificial intelligence (AI) and its subset machine learning (ML) have little to do with human intelligence. AI/ML is all about recognizing patterns in data and automating discrete tasks, from algorithms that flag fraudulent financial transactions to chatbots that answer customer questions. And guess what? IT leaders appreciate the enormous potential.
Recently, I wrote about the difference between long-term support and continuous delivery releases of IBM MQ. In that discussion I pointed out that based on their traditional cadence that a new MQ release was not far off. As expected, IBM has announced IBM MQ 9.2. This is scheduled for availability this week. As I discussed…
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