Digital Transformation And The Road To AIOps

The idea behind writing this post is based on a quick call with one of my college juniors where I was trying to explain her about Artificial Intelligence and it’s possible applications in DevOps. According to her, it’s a system which can be run by any person irrespective of background. That’s perfect!

In my previous post on the dependency of DevOps on AI, we saw that how AI can help DevOps teams develop, deliver, deploy and organize applications to improve the performance and perform the business operations of DevOps. Now if we think deeper into this, we know for sure that ‘DevOps & Cloud’ has already become a promising combination for many companies across the world. Though cloud and DevOps are different propositions, they are intertwined and this combination provides agility and efficiency in IT operations. Automated IT infrastructure is already well-established. Self-healing systems are either not far off with the arrival of containerized orchestration tools (Kubernetes, Docker swarm, etc.). Automated build/deployment of the entire cloud infrastructure using DevOps pipeline in agile fashion is common now. AI will further expand the boundaries of IT infrastructure automation. Future will see intelligent infra powered by sophisticated algorithms using technologies such as machine learning (ML) and deep learning. From an execution point of view, this can be achieved through AI-Ops.

AIOps is the application of artificial intelligence for IT operations. It is the future of ITOps, combining algorithmic and human intelligence to provide full visibility into the state and performance of the IT systems that businesses rely on. Successful digital transformation will rely on AIOps to enable IT to operate at the speed that modern business requires. Promising AI-Ops products as per Gartner should have the following characteristics :

• They should help reduce noise (for example, in the form of false alarms or redundant-events)
• Provide better causality, which helps identify the probable cause of incidents
• Capture anomalies that go beyond static thresholds to proactively detect abnormal conditions
• Extrapolate future events to prevent potential breakdowns
• Initiate action to resolve a problem (either directly or via integration)

However, with the existence of so many DevOps tools, we need to understand that where do AI-Ops tools fit into modern IT Environment. When looking at AIOps for the first time, it is not immediately obvious how it fits into existing categories of tools. The reason is that AIOps does not replace existing monitoring, log management, service desk, or orchestration tools. Instead, it sits at the intersection of these different domains, consuming and integrating information across all of them and providing useful output to ensure a synchronized picture is available from every tool. The whole process will include applying ML models to do the historical data analysis and predict the future of operations on a timeline, highlighting the potential issues and suggesting possible remediation. There could be various manifestations of this troika — AI + Cloud + DevOps; however, we are still at the nascent stage of working with this. But, the basic contour shall consist of embedded intelligence to automate applications/infra, self-learning applications, and in-built governance powered by integrated analytics.

To bring such systems into action, Gartner suggests following optimizations that should be brought in to transform IT-Ops to AI-Ops :

• Deploy AIOps by adopting an incremental approach that starts with historical data, and progress to the use of streaming data, aligned with a continuously improving IT operations maturity.
• Select platforms that enable comprehensive insight into past and present states of IT systems by identifying AIOps platforms that are capable of ingesting and providing access to text and metric data.
• Deepen their IT operations team’s analytical skills by selecting tools that support the ability to incrementally deploy the four phases of IT-operations-oriented machine learning: descriptive, diagnostic, proactive capabilities and root cause analysis to help avoid high-severity outages.

The adoption of AI with DevOps + Cloud is symbiotic. If we look at it technically, the state of software engineering is such that deep learning and machine learning are now progressively becoming mainstream. We no longer need to understand the mathematical jargons like ‘stochastic gradient descent’ or ‘back propagation’ to apply deep learning concepts. We will also not have to write a thousand lines of python code to build a native chatbot. Hundreds of machine learning/deep learning models are now available as managed service on the cloud, along with various AI tools provided by cloud platforms. Cloud providers are trying to make it easy to run the machine learning workloads on their platform. They are offering virtual machines (VM) based on the graphics processing unit (GPU) to build ML applications in the cloud, APIs for pre-built models and natural language processing (NLP) engines to integrate with their applications. Companies are making AI more accessible to the individual developer. AWS sagemaker is one such effort to make machine learning kit available to common developers for building intelligent applications. We will have products/services in-built with machine learning algorithm, like sentiment analysis, predictive algorithms and deep learning models. Prominent ELK stack, Splunk has already seen machine learning concepts infused in their products to identify anomalous patterns, correlate events between infra, application and business environments.

To conclude, the combination of AI, DevOps and Cloud are going to change the way business is conducted across sectors. DevOps and AI will keep moving up in the value chain of technology stack along with Cloud. Intelligent automation will become the new normal, driving new innovations and standards. Enterprises should start finding ways to ingest implicit intelligence into their IT ecosystem.

References :

• Everything You Need To Know About AIOps
• Gartner’s 2018 Market Guide For AIOps Platforms
• Cloud + DevOps + Artificial Intelligence: The New Troika for Intelligent IT Infrastructure

At OpsLyft we are trying to build DevOps tools and platforms powered by AI/ML. We do provide experienced DevOps consulting also. We’d love to hear from you, reach out to us at contact@opslyft.com. I’m sure we’ll fit in somewhere for you.