Five ways Artificial Intelligence is already impacting DevOps
Artificial Intelligence and Machine Learning have gained a lot of media attention over the past few years. Many commentators have pointed out how these new technologies are going to create new and interesting developments in a variety of fields – from law to medicine, transportation to education. At ECS Digital, we see AI and ML having a direct and lasting impact on DevOps, and here’s why.
DevOps is a business-driven approach to delivering software, creating an intense collaboration between developer and operations. Whilst human input remains an important cog within the system, DevOps focuses on encouraging businesses to automate repeatable processes to encourage efficiency, reduce variability and improve quality at every stage of the pipeline.
Emerging AI tools stand to generate even bigger gains. Set to transform how teams develop, deliver, deploy and manage applications, AI and ML perform tasks which would have traditionally required human intelligence. Most notably, these technologies are capable of processing vast amounts of information – picking up the menial tasks and freeing up IT staff to do more targeted work. They can learn patterns, anticipate complications and recommend solutions, all of which fit perfectly within a DevOps culture.
Essentially, AI makes up the technology that integrates into the DevOps systems – affecting both the tools DevOps teams use, and the people who use them.
Here are five ways that AI can work with DevOps to improve software and delivery for the better:
Feedback on Performance
DevOps uses continuous feedback loops at every stage of the process. This involves gathering huge amounts of data in the form of performance metrics, log files and other reports to provide feedback on the operational performance of running applications.
The more advanced monitoring platforms are already applying machine learning to proactively identify problems early in the process and make recommendations. ML in turn is enhancing the continuous feedback loops critical to DevOps by feeding these recommendations straight back to the relevant teams so they can ensure the application service remains viable.
This means you have the 20 highest priority tasks to hand and your AI system can analyse and help pinpoint certain root causes for you to immediately remediate.
Communication and feedback within teams is one of the biggest challenges when an organisation moves to a DevOps methodology. The sheer amount of information within a company’s systems forces companies to reconsider how teams are interacting with one another, with most businesses setting up a wider variety of channels to set and revise workflows as quickly as possible.
Many of our own team have experienced being blocked by administrative tasks whilst helping clients adopt new technology and ways of working. These tasks often take several weeks to complete, delaying progress in projects and momentum of change. “In these cases, it is advantageous to have access to self-service portals or ChatBots that will help me to orientate in customers’ infrastructure” – Marian Knotek, DevOps Consultant at ECS Digital.
AI systems such as ChatBots are essential to supporting the automated technology that DevOps offers, helping these communication channels become more streamlined and proactive.
To operate efficiently, DevOps teams need to simplify tasks. This is becoming increasingly more difficult as environments become more complex. The sheer volume of data in today’s dynamic and dispersed application environments has made it tricky for DevOps teams to effectively gather and apply information that can help resolve customer issues.
Start with monitoring tools for example, teams tend to use multiple tools that monitor an application’s health and performance in different ways. Extensive amounts of data produced by various platforms and tools are usually aggregated by tools like Splunk’s Artificial Intelligence for IT operations solution harnesses log, application, cloud, network, metric data and more. By automating routine practices, accuracy and speed of issue recognition are increased and operations become streamlined.
In a nutshell, Artificial Intelligence and Machine Learning applications are capable of absorbing multiple data streams to find correlations, possible dependencies and issues in the system, giving the team a more holistic view of the application’s overall health.
Alert systems are fundamental to the DevOps culture of ‘fail fast, fail often’. But when a system has been set to flag inconsistencies and flaws in real-time, these can hit the team thick and fast with no differentiation between the severity of the problem – making it difficult for teams to react.
Machine Learning applications can help teams prioritise their responses. Pulling on data such as past behaviour, the magnitude of the alert and the source, DevOps teams can set up rules which enable machines to manage the influx and assort the data when it begins to overwhelm the system.
Improved customer service
Improving the customer journey and providing a positive customer experience (CX) was ranked as the top strategic priority in a survey of global banking organisations for the 2017 Retail Banking Trends and Predictions Digital Banking Report. For many, understanding how users are interacting with their business and tweaking their software in response to these findings is a significant part of creating an all-round better CX. Businesses are also looking for ways to effectively support a 24/7 always on, internet-based, mobile-accessible consumer environment.
Artificial Intelligence and Machine Learning lend themselves perfectly to this landscape. Not only can they collect and analyse data, they can pre-empt questions that may come up during the customer journey and manage the bulk of enquiries to help ease human resource. ITSM tools such as ServiceNow are capable of fashioning a pattern of events before each previous failure is noted. This results in the creation of a support ticket before the event takes place, moving businesses from a reactive to a predictive approach.
This ability to solve a problem before it arises is a huge benefit, significantly lowering customer abandonment rates in the purchasing cycle. It has also been proven to reduce customer complaints and improve consumer satisfaction.
The Future of AI and DevOps
AI, Machine Learning and DevOps – none of these concepts are leaving the conversation any time soon. All are contributing huge amounts to innovation in the tech space and, whilst they are able to operate effectively on their own, there is an interesting dynamic between the maturity of one and the evolution of the others.
The IT industry right now is already in a very different place than where it was five years ago. Whilst DevOps has repeatedly proven its place, this fast development of IT requires reshaping the cultures and mindsets around how we can get the most out of an already successful tool. Most notably, these new approaches towards automated IT enable shouldn’t be ignored. Enterprises that do not make this adjustment and fail to adapt their DevOps efforts to work with Artificial Intelligence and Machine Learning are going to find themselves left behind.
ECS Digital is an experienced digital transformation consultancy that helps clients deliver better products faster through the adoption of modern software delivery methods. We help our clients transform at scale through the use of Enablement Pods – combining outcome focused teams and value-add sprints.
Our Pods deliver DevOps, CT, Cloud and engineering capabilities in one team. This means you get process, enablement and nearly two decades of experience on top of the first-rate engineering, tooling and testing you would expect.
It also means you have a team on board that can help implement the technology you need to embrace Artificial Intelligence and Machine Learning and enable your team in modern tools, technology and ways of working.
Want to know how we can benefit your business? Get in touch.