When it comes to the most powerful and rapidly advancing technologies in the modern-day, machine learning and Artificial Intelligence are leading the pack. These systems are being used to benefit almost every sector of the technology industry, with software development coming as no exception.
As a data-driven investor with an academic and professional background in computer science, business, and law, Dan Calugar is always looking to streamline efficiency and find inventive ways to increase productivity. Machine learning and AI allows for this to happen. These systems help to mine massive complex datasets, detect patterns and antipatterns, uncover new insights into data, iterate and refine existing queries, and repeat these processes continually, all at ‘computer speed.’
Applying these capabilities to DevOps allows the handling of high velocity, volume, and varieties of data. These new delivery processes are poised to become the next generation of atomized, composable, scaled applications.
With AI and machine learning, these applications are not only operated with these processes but can be analyzed to ensure application quality. Machine-driven understanding enables more comprehensive testing on every release, whether the defects are novel or recurring. Thus, the quality of the delivered application is increased, leaving the consumer more satisfied.
Dan Calugar has utilized his technical skillset to design computer programs that work to bring these intelligent systems to life in delivering timely alerts, superior execution efficiency, and swifter failure forecasting.
Smart resource management is possible by implementing machine learning and AI systems into DevOps processes. In this fast-moving technological age, it’s unwise to have people to complete tasks that are automatable and repeatable. By reassigning resource management work to smart systems, employees can spend more time working in the business than on the business.
Developers are required to release code at an extremely high velocity. Therefore, operations teams are tasked with ensuring minimum disruption of existing systems. AI allows for improved collaboration between development and operations teams, transforming the processes and standards of DevOps as an industry.
When applications are having problems, DevOps businesses are negatively impacted. Through the synthesis and analysis of good and bad patterns within applications to provide an early warning system, coders and business teams can act more quickly. Machine learning and Artificial Intelligence, in this case, directly contribute to meeting business goals. Without an understanding of business impacts due to code releases, DevOps cannot succeed.
While there will likely never be a substitute for experience, creativity, and a willingness to do hard work, machine learning and Artificial Intelligence can go a long way in improving DevOps operations. Much of these learning systems are already being applied today, with no limits to innovation insight.
In an industry as ever-shifting and dynamic as DevOps, the most efficient firms will utilize machine learning and AI to their fullest capacity in order to achieve a competitive advantage.