Getting to Zero Touch Intelligent Automation for Application Delivery

The increasing complexity and scale of enterprise cloud almost necessitates automation tools. But workloads change quickly, and the process of keeping up with application configuration and deployment changes is complex and prone to errors. The result is often service interruptions, wasted cloud resources and performance bottlenecks — which can defeat the purpose of automation. Enter Machine Learning. ML is one artificial intelligence technology that is paying big dividends in IT. Your systems contain a mountain of useful application behavior and user requirements data — data that no human can hope to process. But by applying ML to this data, you’re able to identify performance glitches before they affect production, streamline automation processes and discover new, more efficient application deployment strategies. ML-guided automation also lets you manage an enterprise cloud data center intelligently tasks that used to require lengthy, error-susceptible procedures can be reduced to a single click of an ML-generated tool. ML additionally detects and reports unexpected infrastructure events, such as high utilization or change in load patterns, using a variety of reports, dashboards and widgets. Learn the best ML algorithms for IT automation and how they compete in a true ML automation environment to find and implement optimal enterprise cloud application deployment, operations and maintenance strategies. This webcast will cover: - A brief overview of machine learning in the IT management context - The best kinds of data to feed into ML time-series models - How competing ML models run to optimize automation processes - Real-world examples of ML-driven enterprise cloud automation