June 29, 2020 By Eric Carter 3 min read

Explosive growth in cloud applications built on containers has exponentially increased the volume of alerts, signals, and data vying for the attention of IT admins. Applications consisting of dozens of microservices, running across intercommunicating nodes, complicate incident detection and response. To keep pace with highly complex environments, companies need to rethink how to manage incident detection and response to minimize disruption and ensure service availability.

Today IBM announces the availability of IBM Watson AIOps to help IT teams more effectively identify and resolve their most complex issues. Sysdig has collaborated with IBM to bring deep container visibility and rich Kubernetes context together with IBM’s best-in-class artificial intelligence. Together, the Sysdig Secure DevOps Platform and IBM Watson AIOps are able to provide clients with better visibility, both in depth (in and across containers) and breadth (across data sources and types), to reduce operational costs for dynamic cloud-native environments.

What is AIOps?

AIOps—short for “artificial intelligence for IT operations”—applies artificial intelligence, machine learning, analytics and big data thinking to large volumes of diverse data sets, to identify patterns and events and reduce the amount of effort and time needed to pinpoint systems and service issues. The promise of AIOps is to help IT professionals rise above the noise and move beyond the bottlenecks of manual troubleshooting to a more automated, proactive approach.

Using the power of IBM Watson to drive better outcomes

Watson AIOps provides an Explainable AI (XAI) solution that incorporates analysis of structured and unstructured data into a single, unified view to identify patterns and provide insights and recommendations that enable a human to take action. Designed to deliver a holistic understanding of your IT environments, Watson AIOps helps IT teams address complex issues across data centers and clouds in real-time. With it, you have a data science toolchain to manage and govern AI in production.

Solving Kubernetes problems with Sysdig and Watson AIOps

Sysdig helps DevOps teams deploy and run cloud workloads securely, meet performance and availability SLAs, and validate compliance. By incorporating the data insights available from Sysdig, Watson AIOps can apply machine learning and analytics to Kubernetes and container data to spotlight trends, issues and potential problems across the complex dependencies and components that comprise your clusters and microservices.

As IT teams embrace cloud-native technologies to deliver innovation faster and meet market demands, they face a whole new set of operational and security challenges. Tooling and telemetry designed to support legacy applications has proven insufficient in helping IT teams understand what’s happening inside containers and clouds. Sysdig solves this challenge by instrumenting and aggregating a rich set of data sources for containers and Kubernetes. This includes kernel-level system call data, Kubernetes events, Prometheus metrics and other information sources along with Kubernetes metadata for context to give you a comprehensive picture of your cloud-native performance, health, and risk. Click here to explore more about the Sysdig platform and how it works.

Run your cloud with confidence

Together, Sysdig and Watson AIOps break new ground for incident resolution in IT operations for Kubernetes, containers, and cloud-native solutions. Whether you operate across a single cluster in a single location or in a hybrid or multicloud environment, you can now seamlessly integrate AI to help you save time, respond more effectively, and run your cloud with confidence.

We’ve made it easy to get started. Learn more about Watson AIOps and sign up for a free Sysdig trial on IBM Cloud.

Was this article helpful?
YesNo

More from Artificial intelligence

AI transforms the IT support experience

5 min read - We know that understanding clients’ technical issues is paramount for delivering effective support service. Enterprises demand prompt and accurate solutions to their technical issues, requiring support teams to possess deep technical knowledge and communicate action plans clearly. Product-embedded or online support tools, such as virtual assistants, can drive more informed and efficient support interactions with client self-service. About 85% of execs say generative AI will be interacting directly with customers in the next two years. Those who implement self-service search…

Bigger isn’t always better: How hybrid AI pattern enables smaller language models

5 min read - As large language models (LLMs) have entered the common vernacular, people have discovered how to use apps that access them. Modern AI tools can generate, create, summarize, translate, classify and even converse. Tools in the generative AI domain allow us to generate responses to prompts after learning from existing artifacts. One area that has not seen much innovation is at the far edge and on constrained devices. We see some versions of AI apps running locally on mobile devices with…

Chat with watsonx models

3 min read - IBM is excited to offer a 30-day demo, in which you can chat with a solo model to experience working with generative AI in the IBM® watsonx.ai™ studio.   In the watsonx.ai demo, you can access some of our most popular AI models, ask them questions and see how they respond. This gives users a taste of some of the capabilities of large language models (LLMs). AI developers may also use this interface as an introduction to building more advanced…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters