The volume/scope of data that businesses produce and IT pros have to manage has grown rapidly. Managing this onslaught of data can hinder IT productivity, especially when they are hampered by traditional tools and old school approaches. The exponential growth of virtual IT infrastructures in both scale and complexity is pushing IT teams to their limits. However, IT teams are still looking at their virtual infrastructures in individual operational silos – compute, application, storage, and network.
They are using multiple tools to gather information about each silo and then piecing the results together manually. They rely on their own experience to develop a theory about the root cause of performance issues and to devise a strategy for resolution. This inaccurate approach is leaving IT time strapped, stressed out and without clear answers to key questions about application performance issues in virtualized environments, including how to fix them. More and more companies are looking to machine learning based solutions for the answer.
Given the enormous growth of virtualized systems, IT pros can no longer make informed decisions by analyzing alerts from traditional threshold-based analytics and monitoring tools. Similar to the manufacturing revolution of the past, IT pros now need help from machines to be effective in today’s data-driven world. They need a solution capable of simultaneously considering data from across the IT infrastructure silos and applications. A solution that understands the subtle ways that components in virtual environments interact with one another and the changing patterns of their behavior over time. Most importantly, IT pros need advanced machine learning and deep learning tools that do this work for them.
Machine Learning / AI Debate
While debate continues over the effect that machine learning and AI will have on the workforce, there is no denying that IT needs help from machines. This particularly true for IT teams that are managing virtual environments. Machine learning is here to relieve IT of low-value manual work, not replace them. Machine learning analytics tools provide a degree of automation, precision, and accuracy that humans with threshold-based tools cannot approximate. In one click of an advanced ML-based solution like SIOS iQ, they can identify root causes of issues, predicting future problems, and get recommended solutions.
IT pros shouldn’t have to weed through hundreds of alerts or compare dashboards filled with charts to diagnose problems. It’s time-consuming and ineffective. With new, advanced machine learning and deep learning-based tools, IT teams can move from a reactive to a proactive approach. They can shift their emphasis from diagnosing problems to avoiding them in the first place. Freed from the need to over-provision in order to ensure performance and reliability, they can look for ways to optimize efficiency and responsiveness in their data center. They can also use machine learning tools to implement strategies that evolve their environments to support their business’s operations.
The Benefit of Machines Learning /AI
Machine learning enables IT pros deliver fast, accurate solutions to complex problems. IT can spend more time for innovating and less time on trial-and-error. Fortunately, advanced solutions like SIOS iQ, can help IT optimize provisioning, identify performance issues and prioritize problems instantaneously. SIOS iQ learns patterns of behavior of interrelated objects over time and across the infrastructure. It uses patented meta-analysis techniques to predict issues before they arise and recommend precise solutions.