This article is the final installment in a 4-part series on leveraging artificial intelligence and machine learning (ML) for IT operations (AIOps) to provide a more efficient, reliable, agile, cost-effective, and optimized IT infrastructure. Just as our roads and highways evolve overtime to meet the demands of the travelers who use them, AIOps will continue to transform how organizations build, use, and manage their infrastructures.

Your IT teams are critical to improving the efficiency of your operations and ensuring long-term business scalability. But as your organization grows and demands become more complex, the challenges of managing IT operations can become difficult, especially when managing multiple applications across various server environments. Containerization has become a popular solution for some of these challenges.

For most organizations, having a stable and reliable IT infrastructure is essential for success. But managing multiple servers, databases, and applications can often be difficult and time-consuming. Container orchestration is a standard solution for handling this complexity. Docker Swarm has gained popularity as a container orchestration solution because of its simplicity and scalability.

UX (user experience) is a core factor that determines the success of an application or platform in a distributed system. Specifically, developers need to understand the infrastructure within an entire application stack to improve and refine the user experience to meet customer expectations without guesswork. System downtime remains a significant source of revenue and reputational losses for enterprises, employees, and customers.

Modern businesses are constantly looking for more efficiency and better performance in their daily operations. This is why embracing cloud computing has become necessary for many businesses. However, while there are numerous benefits to utilizing cloud technology, obstacles can get in the way. Managing a cloud environment can quickly overwhelm organizations with new complexities.

Every well-oiled machine needs both a gas and a brake pedal. If our article titled How IT Teams Can Leverage AIOps’ Capabilities is the gas pedal in this analogy, then this writing is the proverbial brakes in which we explore some educational pit stops organizations should make on their way to integrating artificial intelligence (AI) and machine learning (ML) into their IT operations (AIOps).

The rise of complex containerized software environments sees an increased need for reliable delivery solutions like application delivery controllers (ADCs). ADCs such as Citrix function as load-balancing intermediaries within a software delivery network. They are positioned between application and user servers where they manage traffic flow via various structured and centralized processes.

As organizations continue to digitally transform and expand their networks via cloud and multi-cloud environments, it has become increasingly critical to protect microservices and data flow. Implementing advanced technology such as service mesh helps your team secure data networks and manage system access policies by matching user intentions to workload states. Service meshes like Istio support the latest software application trends like containerization and microservice infrastructures.

This article is the second in a 4-part series on leveraging artificial intelligence for IT operations (AIOps) to provide a more efficient, reliable, agile, cost-effective, and optimized IT infrastructure. If Artificial Intelligence is the ultimate multi-tool for IT operations (as discussed in our first article), then DevOps, Network Ops, Site Reliability Engineers (SREs), and SecOps are the teams using it.

Keeping up with the speed of business requires the right tools and tech. You expect efficiency gains when moving to and from the cloud, but risks and visibility gaps happen when resources are monitored by separate tools and teams. And since on-premises infrastructure is likely managed by dedicated IT teams and monitoring tools, you can’t clearly see if migrated resources perform correctly. The results involve disconnected visibility, tool sprawl, and increased MTTR.