As technology changes our world, there’s need for an increase in innovation on the system side of work. Today many businesses are looking for high-performance while building better products faster for greater customer satisfaction. A system that allows organizations to serve their customers better and compete more strongly in the market is needed. Hence, the need for ‘DevOps’.
What is DevOps?
DevOps is a portmanteau of “development” and “operations” and can simply be defined as the collaboration between development and IT operations to make software production and deployment in an automated & repeatable way. DevOps is the combination of practices and tools designed to increase an organization’s ability to deliver applications and services faster than traditional software development processes. This speed enables organizations to better serve their customers and compete more effectively in the market.
DevOps is all about automation of tasks. It focuses on automating and monitoring every step of the software delivery process and ensures that the work gets done quickly and frequently. While it may not totally eliminate human tasks, it does encourage enterprises to set up repeatable processes that enable promoting efficiency and reducing variability.
According to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty. To put it simply, DevOps can help businesses compete in already congested marketplaces. Hence the need arises for organisations to consult and contract services of IT companies.
Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams.
Why is DevOps needed?
There is a demand to increase the rate of software delivery by business stakeholders, hence the need for DevOps as an essential ingredient in the growth of a company. The following are the reasons why DevOps is needed:
- Before DevOps, the development and operation team worked in complete isolation. Testing and Deployment were isolated activities done after design-build, but with DevOps it’s now done in unison. Hence they will consume lesser time than the actual build cycles.
- Without using DevOps, team members are spending a large amount of their time in testing, deploying, and designing instead of building the project.
- Manual code deployment leads to human errors in production.
- Coding and operation teams have their separate timelines and are not in sync causing further delays. Hence DevOps is needed to provide the much needed synergy for faster and more efficient service delivery.
Machine Learning and DevOps
Successful DevOps practices generate large amounts of data, and this data can be used for such things as streamlining workflow and orchestration, monitoring in production, and diagnosis of faults or other issues.
But this system generates too much data. Server logs themselves can take up several hundred megabytes a week. If the group is using a monitoring tool, megabytes or even gigabytes of more data can be generated in a short period of time.
Monitoring an application produces server logs, error messages, transaction traces—as much and as frequently as you care to collect. The only reasonable way to analyze this data and come to conclusions in real-time is through the help of machine learning. Machine learning is considered to be a perfect fit for a DevOps system. They are able to process vast amounts of information and help to perform menial tasks, while freeing the IT staff to do more targeted and strategic work. They also learn patterns, anticipate problems and suggest solutions to issues.
How can DevOps with Machine Learning help to drive an Enterprise
Machine language driven DevOps can change your organization in many ways, from teasing out valuable insights from data to reducing manual work, with the agreement that your IT-organization is machine learning ready; that is, it has a strong DevOps infrastructure with a sophisticated data and machine learning foundation on top.
The following are ways in which machine learning with DevOps can be beneficial to your Enterprise:
1. Focuses on finding thresholds and analyzes data.
Due to the large chunks of data, DevOps teams rarely view and analyze the entire data set. Instead, they set thresholds, such as “X measures above a defined watermark,” as a condition for action.
Machine learning can help in predictive analytics as it’s processes the entire data, and makes reasonable remarks.
2. Looks for trends rather than faults in data.
When traditional monitoring and analytics fail, data can be fed to ML algorithms that are capable of finding the relationship between multiple variables, to find and predict trends rather than looking at sporadically occurring results of these trends. Basically, DevOps fix errors but don’t look into the vast majority of data that may include clues as to why those errors have occurred in the first place.
3. Enabling Continuous Feedback Loops
Machine learning algorithms can process and analyze any amount of data to identify problems and come up with recommendations in advance, which allows human workers to maintain and support the system. Therefore, machine language can empower DevOps through feedback analysis and enrichment along the entire DevOps lifecycle.
4. Analyze and correlate across data sets when appropriate
Much of your data is time-series in nature, and it’s easy to look at a single variable over time. But many trends come from the interactions of multiple measures. For example, response time may decline only when many transactions are doing the same thing at the same time.
These trends are virtually impossible to spot with the bare eye, or with traditional analytics. But properly trained machine learning applications are likely to tease out correlations and trends that you will never find using traditional methods.
In conclusion, organizations that want to automate the DevOps first need to establish a strong DevOps infrastructure by consulting and contracting IT companies. Once the foundation is created, then machine learning is applied for increased efficiency. Machine learning assists the DevOps teams to focus on creativity and innovation by eliminating inefficiencies across the operational life cycle. It enables teams to manage the amount, speed and variability of data. This, in turn, results in automated enhancement and an increase in DevOps team’s efficiency. Interventions by machine learning on DevOps will not only make code development, deployment and production runs much more predictable, but also provide a continuous innovation process for technology Enterprise.
