Redgate this week previewed machine learning capabilities that it is adding to its test data management and database monitoring platforms that are scheduled to be made generally available in the first quarter of 2025.
Using machine learning algorithms to discover patterns, relationships and distribution characteristics within data, Redgate Test Data Manager will generate synthetic data that mirrors those properties. That capability makes it simpler for application development teams to create data for testing applications without having to copy any data running in a production environment.
Additionally, that data can also be used by data science teams to train artificial intelligence (AI) models without having to worry about where sensitive data might inadvertently be exposed.
At the same time, the company is adding machine learning algorithms to Redgate Monitoring that will generate alerts based on patterns discovered in metric data. It will then match alerts to the real usage seen on monitored databases to make alerts more relevant and reduce overall alert fatigue.
That capability provides the added benefit of making it simpler to tailor Redgate Monitor to each IT environment without having to manually configure and maintain alerts.
James Hemson, a product manager at Redgate Software, said that while the company continues to research generative AI technologies it’s clear that machine learning algorithms provide a form of AI that is of most use to IT teams. For example, IT teams will be able to better predict how much CPU utilization will be required for the databases that have been deployed, he noted.
A recent Redgate survey finds that 65% of respondents are employed by organizations using AI for tasks such as generating sample data or code snippets, optimizing database queries and automating the creation of tests. A Techstrong Research survey finds a third (33%) are working for organizations that make use of artificial intelligence (AI) to build software, while another 42% are considering it. Only 6% said they have no plans to use AI.
Only 9%, however, have fully integrated AI into their DevOps pipelines. Another 22% have partially achieved that goal, while 14% are doing so only for new projects. A total of 28% said they expect to integrate AI into their workflows in the next 12 months.
It’s not clear to what extent various types of AI models will transform software engineering but the pace at which software is built, tested and managed is starting to accelerate. Tasks that used to require days and weeks to complete can now be accomplished in minutes and hours. Those advances are not likely to eliminate the need for DevOps engineers but roles within those teams will be evolving as more manual tasks are completed.
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Hopefully, as the overall level of manual toil continues to decline the overall rate of burnout that many software engineers experience will rapidly decline. In the meantime, savvy DevOps teams should be making a list of the manual tasks that AI either can or soon will automate to better understand how software engineering teams might need to be restructured tomorrow.
