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Author: Rahul Jha |07/16/18

Cloud + DevOps + Artificial Intelligence: The New Troika for Intelligent IT Infrastructure

The idea behind writing this blog is based on an informal conversation with a friend whose definition of intelligent system is – “It can be run by any dumb person!”

Taking a cue from the above wit, let’s try conceptualizing the intelligent infrastructure system.

Can a person set up an entire IT infrastructure without knowing anything - probably with few lines of English text or speech; and run IT operation without knowing much about it - by using simple chat or voice instructions? The answer is YES and is subject to imagination & technical restrictions.

The answer is a combination of Cloud, DevOps and Artificial Intelligence (AI).

The need of intelligent ways for IT infrastructure

‘IT Infra & Operation’ is going through a major overhaul in the contemporary days due to digital transformation driven by new technologies such as cloud computing, automation, machine learning, IoT and containerization. With growing workloads, increased pace of innovation, exponential data growth, and users in system (IoT, machine agents), conventional IT is struggling to cope up with the new demands. At the same time, we cannot afford to have multiyear system implementation. Hence, it is imperative to infuse software intelligence in building modular and fluid architecture with a mindset of Cloud First, DevOps First, AI First (Not yet arrived fully).

Let's now understand the basic contour of these to venture into new fascination:

Cloud – Adoption of Cloud First is already in place. Cloud technology has helped in accelerating every aspect of IT infra – solution, delivery and operations.

DevOps - It is clear that DevOps is evolving rapidly, though the direction and destination of evolution is still open to guesses. DevOps is meant for simplifying and automating all aspects of software delivery process, particularly in automating infrastructure - Infra as a Code (IaaC).

‘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.

Going beyond Cloud and DevOps with AI

AI is here to apply intelligence that will lead to continuous innovation which is a step forward to continuous integration and delivery.

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. Machine learning will also help in giving way to intelligent CI/CD pipeline.

Ideal amalgamation would be to have AI-OPS tool that proactively detects the cloud infra requirement and optimally manages the demand using automated DevOps pipeline. 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.

Use cases in IT infra

  • Unsupervised machine learning on massive data (produced by disparate systems) - Generate correlation of events, recognize patterns and detect anomalies
  • Forecast or predictions – Determine when a metric will hit the threshold, perform ‘what if’ scenarios, and take precautionary actions before a failure happens
  • Root cause analysis – Continuously corelate data points and pinpoint to possible problem and its remediation
  • Cost optimization (use case of cloud migration) – Given various input parameters and known output of lowest cost, a supervised ML model to generate cost-optimized cloud migration plan can be built in all probabilities
  • Noise reduction - Lessen noise from the plethora of alerts, events, logs and simplify the workflows using ML models
  • AI based DevOps analytics - Generate the operational metrics to effectively measure the success of DevOps implementation (AI in DevOps)
  • DevOps principle of iterations - Speed up the process to train AI algorithm on the ever-changing AI Models (DevOps in AI)
  • NLP based ChatOps - Build a new environment, spin up a new resource in cloud or generate on-demand infra utilization metrics (AI in Cloud infra)

Symbiotic adoption of AI, Cloud, DevOps

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 thousand lines of python code to build a native chatbot. Hundreds of machine learning/deep learning models are now available as managed service on 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 graphics processing unit (GPU) to build ML applications in the cloud, APIs for pre-build models and natural language processing (NLP) engines to integrate with their applications. Companies are making AI more accessible to 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, corelate events between infra, application and business environments.

To conclude, the combination of AI, DevOps and Cloud is 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. We all need to be prepared to embrace this new technology wave – A future survival kit!

Wondering if this troika (AI +DevOps+ Cloud) can predict, act, resolve and manage businesses better than human in the future?


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