Observability vendors like to market their tools as magic, getting data out of thin air to keep applications operating properly. Even so, many problems still persist, come as surprises and take too long to solve. It can seem like the only possible solution is to keep replacing your monitoring solutions. There IS a better way! Don’t toss out your monitoring. Just make better use of your data.

In this presentation, InsightFinder Principal Solution Architect Arun Jain will show you a better way to manage your management data including:

• Reasons why your monitoring tools remain reactive
• Key AIOps methods and capabilities you should be implementing
• Real world examples showcasing incident prediction and outage prevention

Ultimately, you’ll learn how you can use the data you already have to actually attain zero downtime. Now that would really be magical.

Monitoring has been used for decades by IT teams to gain insight into the availability and performance of systems. However, teams today require a deeper understanding of what is happening across their IT environments. Modern infrastructure and applications can span multiple domains, are more dynamic, distributed and must support ongoing change. In this atmosphere, it is more difficult than ever to consistently maintain SLOs. Further, many enterprises are using more than 10 monitoring tools running as siloed solutions. The result? IT is unable to proactively detect and quickly diagnose and address issues, especially when they cross boundaries.

Based on research and conversations with enterprises from various industries, StackState created the Observability Maturity Model. This model defines the four stages of observability maturity. The ultimate destination is level four, Proactive Observability with AIOps. However, even moving from level one to two, or from level two to three, is a huge improvement in your ability to get essential insights into your IT environment.

Please join us and Lodewijk Bogaards, StackState’s co-founder and chief technology officer, on Wednesday, September 21 for a lively discussion on:

• The four stages of observability maturity
• How to determine the current stage of observability maturity within your own organization
• The steps to take to advance your observability maturity and the value to your organization in doing so

Cybersecurity teams remain chronically overworked and short-staffed. It’s clear that AI must play a role in augmenting these cybersecurity teams, but there’s still a long way to go before AI models help level the playing field between cyberattackers and defenders. Many of the routine analytics tasks that are performed manually today will increasingly be automated, which will increase the amount of time teams have to discover and thwart more sophisticated threats. And AI has a role to play in cybersecurity hiring and talent retention, too—savvy cybersecurity professionals may not want to work for organizations that don’t invest in the AI technologies that can help them succeed.

When artificial intelligence (AI) first arrived on the cybersecurity scene, there was understandably a lot of skepticism. Grandiose claims about the ability of AI platforms to eliminate the need for security analysts have given way to more rational assertions that focus on the role machine learning algorithms will play in augmenting the skills—and easing the strain on—cybersecurity professionals. The conversation has evolved as understanding has grown about what AI and machine learning can and cannot do in a cybersecurity context. Now, it isn’t so much about whether AI will replace cybersecurity professionals as it is about how quickly AI models based on machine learning algorithms can be applied to help thwart attacks.

Join us as we discuss:

• The current state of AI and ML in security
• The future state of AI and ML in security
• What organizations can do now to prepare for the next wave of AI in ML in cybersecurity, from both a threat perspective and protecting against those threats

In academic circles, the promise of AI is obvious. But there’s a big difference between the theoretical possibilities of AI/ML and making it work in the real world. Even the latest and greatest new AI model that beats every key benchmark is worthless if it doesn’t respond fast enough or won’t run with little memory in a real-life situation. The ivory tower of academia fuels innovation, and there are plenty of hypotheticals churned out every day. In reality, deploying industrialized AI in production faces a number of challenges. In this webinar, we examine the challenges involved in making a state-of-the-art model work for you in highly scalable applications.