DevOps AI & Data Science: A Key to Business Outcomes
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DevOps, AI and Data Science: A key to amazing Business Outcomes

Published datePublished: Jan 25, 2021 Last Updated Last Updated: Nov 6, 2023 ViewsViews: 2893
Mukul Mayank

Mukul Mayank

Mukul boasts a proven track record in delivering innovative solutions within budget and on time, meeting clients' objectives while opening new business opportunities. In his role as COO at TechAhead, Mukul's visionary leadership is the driving force behind the company's success in the ever-evolving tech industry.
DevOps, AI and Data Science: A key to amazing Business Outcomes

No doubt, DevOps is the rising star of the software industry. However, the orchestration in DevOps demands advanced tools and technologies to give the best results. The amalgamation of AI and data science is transforming DevOps and revolutionizing software development practices.

Researchers at Gartner forecasted that 40% of DevOps teams would augment application and infrastructure monitoring apps that have integrated artificial intelligence for AIOps by 2023. DevOps teams that are using requirements management platforms driven by AI and ML (Machine Learning) can save a significant amount of time which can help them focus more on creating software products within the tight deadlines.

Challenges in DevOps Implementation

DevOps is a pinnacle that every other organization is trying to reach as the focus is shifting from development to delivery. However, managing a DevOps team require strenuous efforts to handle the magnitude of data circulating within the dynamic application environments.

The challenges organizations face with DevOps are data security risks, inconsistencies in testing processes, outdated Legacy modernization systems, lack of communication, etc. However, having clarity regarding the practices and underlying principles you need to apply to resolve them can sail you safely through the storm.

The most critical challenge in implementing DevOps efficiently is adapting to new technologies for streamlining software development, testing, and deployments across different departments within the organization.

AI has the potential to streamline DevOps and production cycles and address many areas of release and entities of DevOps. It can accelerate DevOps functioning significantly while reducing both cost and time to market.

Why DevOps, AI, and Data Science Need to be Tightly Aligned?

The value of AI to an organization is contingent on the relationship build between DevOps and data science. In many organizations, it is crucial to use AI and ML with DevOps to ensure continuous delivery of high-quality applications. Infusing AI in testing and operations brings efficiency in detecting critical problems and helps facilitate DevOps enhancement.

DevOps and data science share a powerful alliance with several related capabilities such as AI, Operational Analytics, Predictive Analysis, and Algorithmic IT Operations. Introducing machine learning into DevOps simplifies the use of highly complex data sets to a large extent. For example, it presents a better testing pattern based on QA errors, detects irregularities related to malicious activities, and refines queries with speed and perfection. In addition to that, Integrating DevOps with ML can uncover anomalies in data and help in identifying inefficient resourcing, process slowdowns, and excessive task switching.

Accelerating Automation

AI brings both automation and consistency into DevOps. With AI facilitating automation processes, not only reduces the chances of human errors but also frees up valuable resources that can be utilized in innovative solutions and other important segments. Additionally, AI has the power of self-healing problems and recommending solutions that can help the coders write more efficient and quality codes.

Getting the requirements right up-front helps keeps an entire project on track with its project plan.

Streamlining requirements management leveraging AI delivers solid results in improving the quality and accuracy of the required documents.

As the market demands continuous growth and evolvement, software organizations need to rebrand and reposition themselves as AI-powered. This will help the companies strike a balance between infusing AI in existing modules and developing independent AI portfolios. By anticipating what developers need in advance, AI and ML can help accelerate every phase of DevOps development cycles.

Bottom Line

DevOps is a recent addition to the industry, and its scope will expand even more with the application of AI and data science. TechAhead’s DevOps consulting services are equipped with the latest tools to deliver agile applications for its clients. Consultants at TechAhead have expertise in integrating IT into the core of your business and have assisted 100+ clients in adopting DevOps practices for their organization. If transforming your business with DevOps is in your mind, feel free to reach experts at TechAhead.

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