Machine Learning For Your NOCs and IT Operations Teams
Rapidly increasing IT complexity, customer expectations around application availability and performance, and the importance of supporting new digital initiatives and services, taken together, are placing unprecedented demands on Network Operations Centers (NOCs) and IT Operations teams inside large, complex organizations like yours.
Machine learning can help NOCs and IT Operations teams cost-effectively scale and meet these demands by helping them autonomously respond to incidents 24 hours a day, seven days a week, 365 days a year.
However, before you jump into the world of machine learning and artificial intelligence-driven IT Ops tools (also sometimes referred to as AIOps tools) and arm your NOC with them, you should be aware of common problems and how you can overcome them.
Problem 1: A Lack Of Transparency
Too often, machine learning (ML) powered IT Ops tools are opaque. This could not only make it hard to agree with the results, but it could also make it hard to act on them. To overcome this problem, enterprises must select ML-powered IT Ops tools that express their logic in a format that humans can see and understand.
Problem 2: The User Has No Control
The second significant issue enterprises face is that many ML-powered tools don’t let you run edit, preview and test their logic. So you can’t incorporate your business/tribal knowledge, you can’t test the logic against past data sets and you can’t run “what if” experiments. To overcome this problem, enterprises must select ML-powered IT Ops tools with human-friendly control panels designed to give your team control at all times.
Problem 3: Not Easy To Build Trust
The final and potentially the most pernicious problem is one of trust. Organizations belatedly realize, months after investing in a new ML-powered IT Ops tool that adoption is tepid. Users in the NOC and outside the NOC don’t full trust their new tool so they can’t confidently adopt and use the new tool. Often, this is because many ML-powered IT Ops tools are not deterministic (the same inputs result in different results at different times) and because they are opaque black-boxes.
Open Box Machine Learning-driven IT Ops tools can help enterprises overcome all of these problems.
Because such tools are fully transparent, controllable and trustworthy, L1s, L2s, and L3s, as well as anyone else who exists outside of the NOC can learn to trust and depend on them as they carry out their mission-critical activities.
At BigPanda, we help NOC and IT Ops teams inside some of the largest, complex and dynamic organizations in the world successfully, scalably and cost-effectively support their applications and digital services.
Interested in learning more about BigPanda and how we can help your NOC and IT Ops teams succeed?
Watch our webinar, “Machine Learning CAN Help Your NOC Win the Battle…Just Keep These 5 Things in Mind” here.