The future of DevOps is automated. With exponential advancements in Artificial Intelligence and its underlying machine learning technology, the DevOps future is closer than ever. From build and testing to release and deployment, AI is offering more efficient pathways to DevOps optimization powered by machine learning. We, at Oodles, an AI Development Company, discuss substantial applications of machine learning in DevOps to accelerate and enhance development outputs significantly.
A typical DevOps team uses numerous tools and frameworks to monitor and test application performance. Streams of data pouring from these sources create a complex workflow, impeding development cycles with alert noise and inefficient root cause analysis.
Machine Learning Development in DevOps curates and analyzes complex data streams from multiple monitoring tools to identify precise data correlations.
With the ability to parse through structured and unstructured data, machine learning algorithms can utilize data from different tools to extract-
a) In-depth insights about the application’s health
b) Patterns and trends across development errors, and
c) Correlations between delivery velocity and total bugs found
In addition to identification, machine learning can prioritize alerts from monitoring tools to assist organizations in reacting to important alerts more efficiently. This results in 46 times more frequent code deployments and 2,555 times faster commit to deploy lead times- reveals The 2018 State of DevOps Report by Puppet and Splunk.
One of the oldest DevOps alert techniques is monitoring set thresholds or rules signifying error rates above which system failure occurs. For instance, trigger actions if the busy rate of eCommerce storage reaches above 60%. However, the problem with threshold monitoring is that they require herculean manual efforts to set accurate thresholds for every parameter.
With machine learning in DevOps, data collected during threshold monitoring can be used to train algorithms for deeper analysis.
While correlating issues with new deployments, ML engines can detect development anomalies to trigger preemptive actions that save cost and time. AI’s predictive analytics capabilities can analyze delivery processes while detecting early errors in metric values, metric rate of change, and metric patterns.
Historical data is the fuel to run and improve machine learning outputs. In a DevOps culture, performance data from past application processes can enable ML models to improve the development and deployment of current applications.
Machine learning can provide appropriate recommendations about actions testing methodologies, and performance standards for even specific metrics.
With data-driven recommendations, machine learning in DevOps improves IT efficiency, optimizes specific tasks, and streamlines resource planning.
Continuous feedback is the most essential requirement to improve application performance based on the reports of user experience. A vigilant eye is imperative to ensure that DevOps teams work proactively on user feedback to improve UX and customer engagement.
Any amount of feedback can be turned into opportunities for better design with machine learning.
Machine Learning algorithms break down feedback into actionable data by extracting user sentiment, application behavior, and pain points.
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We, at Oodles, are a seasoned team of AI developers and software architects with expertise in deploying emerging technologies. We optimize software development cycles by deploying the latest tools and frameworks like Kubernetes, Puppet, Nagios, Jenkins, and Docker across operations. Our team has successfully delivered on various machine learning solutions including predictive analytics, recommendation engines, and more using adaptive systems.
Collaborate with our AI and DevOps team to optimize your software development cycles comprehensively.