DevOps is expected to grow substantially in the coming years, with an anticipated annual increase of 25% between 2024 and 2032. Integrating AI and ML into the software development life cycle is a major growth driver. It improves predictive analytics, automated testing, and intelligent monitoring.
Enhanced productivity remains a core focus, driven by automation of repetitive tasks and streamlined workflows. The alignment of DevOps with cloud and microservices architectures is set to continue, offering scalability, resilience, and rapid innovation. Quality assurance will be paramount, with rigorous testing and real-time monitoring integrated into DevOps workflows. Security will advance with access controls, encryption, and AI-driven enhancements in version control systems. To be successful in 2024, DevOps must balance the speed of technology adoption with robust security and quality practices.
8 major DevOps trends for 2024
Data will be at the heart of DevOps trends in 2024. AI and ML will glean insights from vast data streams, optimizing resource allocation, predicting potential issues, and driving continuous improvement. Let’s focus on the eight key trends influencing software development in the coming year.
1. GenAI adoption in AIOps
AIOps, the growing DevOps trend of applying AI to IT operations, will continue to grow. Currently valued at $1.5B, the market is expected to expand at a compound annual growth rate of around 15% through 2025. The key trends driving this growth include:
- Generative AI integration in AIOps tools
Integration of Generative AI tools can enhance platforms by improving anomaly detection, root cause analysis, and automated remediation. Experiments indicate that large language models (LLMs) can accurately identify root causes when provided with contextual data, potentially leading to significant improvements in Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) for multiple incidents.
- Focus on data quality and trust
Data quality and enrichment are critical for ensuring the accuracy of generative AI models. Synthetic data generation is gaining importance, enabling faster model development and evaluation. Only 50% of enterprises utilize synthetic data for AIOps, indicating untapped potential. Trust in existing AIOps capabilities is the foundation for adopting generative AI features.
2. DevSecOps: deepening integration of security and DevOps
DevSecOps, a crucial part of DevOps trends, helps identify and mitigate high-risk issues of the DevOps system, combining elements of application development, operations, infrastructure as code, and cybersecurity within the CI/CD pipeline. The primary focus is automating and monitoring security throughout the software development lifecycle. DevSecOps promotes a proactive and collaborative approach to security, resulting in more secure applications, faster development cycles, and reduced production issues. Recent research highlights the growing adoption of DevSecOps, with 37% of organizations extensively incorporating security into their DevOps processes and 33% doing so on a limited basis.
Integration of security and DevOps is expected to deepen, addressing several key improvement areas. These include providing security training to software developers and DevOps personnel, authoring security stories during the Agile software development, establishing consistent central policies for integrating security into DevOps, supporting security team members in pursuing cloud security training and certifications, and tasking developers with implementing security use cases. DevSecOps, marked by continuous enhancement, is seen as a vital defense against the challenges posed by insecure software.
Keep reading: 5 best practices for building an effective CI/CD pipeline
3. Alignment of DevOps with serverless architecture
In the coming year, many DevOps teams are expected to embrace serverless architecture for deploying their applications, using services like Google Cloud Functions, AWS Lambda, and Azure Functions. This strategic shift aims to optimize resource utilization and cost efficiency through improved management, updates, and configuration of servers. Moreover, it anticipates accelerating development processes, elevating application performance, and the frequency of real-time updates and maintenance tasks.
4. Implementation of MLOps in DevOps
MLOps aims to address the challenges of deploying and managing ML models in production. These challenges lie at the intersection of data science, ML engineering, and DevOps. While MLOps draws inspiration from DevOps and shares common principles, it focuses on the unique demands of ML software.
MLOps extends its reach beyond IT operations to diverse spheres, such as enterprise search and broader business contexts, involving collaboration between various teams, including data scientists and data engineers.
Despite these parallels with DevOps, MLOps grapples with unique challenges, including data quantity and quality, model retraining, and the need for extensive tooling and infrastructure. The stakes are high, as the unpredictable performance of ML models can raise ethical concerns and necessitate complex post-deployment stages.
As organizations increasingly adopt AI and ML, the need for a well-defined MLOps framework becomes apparent. The maturity in building ML stacks is still evolving, and businesses must evaluate their readiness, considering factors like business needs, data availability, and technical resources.
5. Automating DevOps with NoOps
NoOps emphasizes reducing operational requirements through automation, which aligns with the broader objectives of automation and cloud migration. Automation is pivotal, enabling the creation of self-healing systems and clear alert mechanisms.
This approach minimizes the need for manual intervention in addressing software issues post-deployment. However, the role of operations remains essential, particularly in handling processes like user management and help desk functions. While automation can streamline certain tasks, inherent human elements, such as onboarding, require operational involvement.
Cloud adoption significantly contributes to NoOps, reducing operational demands across various software layers. Yet, operational teams continue to play a vital role in vendor management and business communication.
6. Application of GitOps in DevOps
GitOps represents a distinctive approach in the realm of DevOps, differing from previous methodologies in its core principles and practical benefits. It extends well-established DevSecOps practices like version control, collaboration, compliance, and CI/CD to infrastructure automation. GitOps applies these principles to various forms of infrastructure, including VMs and containers, making it particularly effective for teams managing Kubernetes-based infrastructure.
What sets GitOps apart from other DevOps trends is its developer-centric orientation, where infrastructure management seamlessly integrates into the same version control system as application development. This fosters centralized collaboration and leverages Git's inherent capabilities. GitOps offers practical advantages such as enhanced efficiency, security, developer experience, cost reduction, and faster deployments.
Organizations achieve improved coordination, fewer errors, and faster issue resolution by unifying infrastructure and application development lifecycles within a single tool. Moreover, GitOps facilitates the adoption of containers and microservices while ensuring consistency across all infrastructure components.
The future of GitOps includes deeper integration with DevSecOps workflows, enabling application and infrastructure management through a unified process. This integration will provide valuable insights for tailoring infrastructure configurations based on application behavior and vice versa.
7. Improving collaboration with ChatOps
ChatOps facilitates a collaborative DevOps environment where teams and tools converse, resolve issues, and drive development processes—all within a shared conversation space. In 2024, ChatOps will evolve from mere communication into a powerful hub for DevOps orchestration. Fueled by advanced AI and natural language processing capabilities, chatbots will seamlessly integrate with CI/CD pipelines, enabling real-time troubleshooting, deployment commands, and proactive issue resolution—all through the familiar interface of messaging platforms. This conversational interface unlocks accessibility for non-technical stakeholders, promotes collaboration across teams, and fosters faster response times. Imagine resolving a production error by simply typing "restart server" in your team's chat— that’s the future of ChatOps, blurring the lines between human and machine interaction for a more agile and efficient DevOps.
8. A shift from DevOps to DevEx
As tech talent becomes critical, companies shift from the tool-focused DevOps approach to a broader DevEx mindset, reports Deloitte. DevEx prioritizes developer experience by providing seamless platforms, efficient workflows, and a positive culture, boosting productivity, happier staff, and better software and customer experiences. With automation and low-code tools on the horizon, DevEx may even reshape the workforce, empowering citizen developers and freeing up skilled engineers for advanced innovations. With 81% of companies seeing profitability gains from DevEx investments, the future promises even deeper integration between internal tech and business strategy.
When developers have the tools and environment they need to be productive and happy, they can create better software faster, ultimately leading to better business outcomes. This shift calls for a deeper integration between the internal tech teams responsible for DevEx and the overall business strategy. Imagine developers understanding and actively contributing to business goals and leaders prioritizing technology investments that empower developers. This alignment ensures that technology advancements directly support broader business objectives, maximizing the value of both DevEx and overall business strategy.
EBOOK
Assess your organization's DevOps maturity: explore models, steps, and metrics in the guide.
Success!
Read more: Tips to find the best DevOps engineers
Wrap-up
The trajectory of DevOps in the years ahead promises substantial growth, from a market size of $10.4B in 2023 to $25.5B by 2028. You must have the right tools and insights to capitalize on this surge. This is where the eight key DevOps trends we've explored become your map to success. From AI-powered automation to cloud-native agility and citizen developer empowerment, these game-changers offer the potential to revolutionize your software delivery process. You can unlock unprecedented speed, efficiency, and innovation levels by prioritizing DevEx, integrating security from the ground up, and embracing the power of microservices and cutting-edge technologies.
Elevate your software development journey to new heights with a trusted DevOps engineering partner by your side. At N-iX, we understand the ever-evolving landscape of technology and the critical role of DevOps in achieving faster, more reliable software delivery. With our expertise, we offer you the opportunity to revolutionize your development processes and streamline your entire production infrastructure.
Have a question?
Speak to an expert
