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451 Research: Gain Intelligence Through SaaS-Based Monitoring and Machine Learning

The ability to analyze data across customers in order to inform their offerings is emerging as a potentially significant differentiator between monitoring vendors that have SaaS deployment offerings and those that don't. While some vendors pursue this opportunity, others are waiting on the sidelines, uncertain about privacy implications.

How CA Operational Intelligence enables IT operations teams to make smarter decisions -- Live Demo

See how CA Operational Intelligence enables IT operations teams to make smarter, faster decisions for enhancing user experience and improving IT service quality and capacity through mainframe to the cloud contextual intelligence. In this demo, you'll see how machine learning-driven analytics, along with out-of-the-box visualization and correlation, can help you drive a superior user experience and boost operational efficiency by up to 60%. Live demo presented by Adeesh Fulay, Director, Product Management

Can You Trust Machine Learning In IT Operations?

Chronically understaffed and constantly stressed-out IT Ops and NOC teams are overwhelmed by today’s IT noise. Artificial Intelligence (AI) and Machine Learning (ML) can help these teams because ML (and AI) are exceptionally good at processing enormous volumes of very complex data in real-time, or near real-time, and surfacing actionable insights.

PagerDuty API Introduction

Learn how easy it is to get up and running with the PagerDuty API in just a few minutes. Harness automation in your incident response and digital operations by leveraging PagerDuty’s REST based API. This video covers basic concepts regarding APIs, REST and JSON. You will also be introduced to PagerDuty’s industry leading interactive API documentation that will automatically provide executable API code at your fingertips.

Machine Learning in IT & Digital Operations: Why Now, And What to Keep in Mind

You’ve just recovered from a critical application outage and your team is being asked to report on root cause and recommended remediation steps later this afternoon. Can you quickly analyze all the data, identify all the leading events, and discern which one was responsible for the cascading failure?