Is everything gradually becoming DevOps?

As companies strive to enhance their software development and operations, DevOps is becoming an increasingly crucial concept.

According to the 2023 DevOps Report, 55% of CEOs sacrifice security and code quality to deliver software quickly. This approach is incorrect since the final product may not fully satisfy customers’ needs. Therefore, more and more companies are turning to the DevOps methodology, which helps to produce a quality product quickly.

The value of DevOps is constantly growing because the IT industry is undergoing a paradigm shift. Customers now demand not only fast updates but also security. The complexity of software is increasing, and technology is constantly evolving. Moreover, the shift towards the Software-as-a-service (SaaS) model has made many companies heavily reliant on the cloud.

It’s now more important to understand the general concepts of DevOps rather than know the syntax. DevOps isn’t just a set of tools or technologies. It’s a culture, mindset, and location of practices that promote collaboration, communication, and continuous improvement between development and operations teams.

Although knowing the specific syntax of some DevOps tools can be helpful, it’s insufficient to comprehend DevOps principles fully. To fully take advantage of DevOps, teams need to understand the overall goals of the methodology, such as:

• increased flexibility
• improved software quality
• faster time to market

The DevOps space is witnessing an increasing number of automation tools that accelerate processes while ensuring the quality and reliability of software. Consequently, having a solid understanding of DevOps principles is more crucial than being an expert in a specific tool or technology.

We pay attention to developing concepts as much as possible. Therefore, today we will talk about DevOps and its role in the modern world, discuss methods for improving productivity and reveal other vital issues.

The concept philosophy of DevOps

We already mentioned that DevOps could be considered an entire philosophy encompassing various methodologies. Generally, DevOps is a process that brings people, technologies, and operations together. The concept aims to expedite the development of high-quality software.

The DevOps model integrates the work of the development and operations teams. Both teams will no longer work as separate entities; they must become one complete mechanism controlling the development process. DevOps emerged in the Agile world in 2001, coinciding with the rise in popularity of Scrum as the most widely used agile framework. Scrum is a methodology designed to facilitate team collaboration in software’s rapid development through sprints – small, achievable tasks that must be completed within a designated timeframe.

However, at that time, Agile teams needed more engineering methods. Therefore, the processes for automating operations and functions of the infrastructure separated from Agile and began to evolve until it turned into DevOps.

As a result, DevOps can be part of software deployment, regardless of the team’s methodologies. Here are some key concepts that are central to DevOps:

1. Infrastructure. DevOps teams use automation tools to provide infrastructure, including servers, storage, and network resources. Infrastructure as code (IaC) is a core concept of DevOps.
2. Automation. Automation is a critical component of DevOps, allowing teams to automate repetitive and time-consuming tasks. It includes everything from preparing the infrastructure to testing and deploying the software.
3. CI/CD. Continuous Integration/Continuous Delivery (CI/CD) is a set of techniques that enable teams to release code into production quickly and confidently. It includes automating build, test, and deployment processes to deliver high-quality software faster.
4. Monitoring. DevOps teams use dedicated tools to monitor the performance and availability of their software and infrastructure. It allows them to identify problems and take action to resolve them quickly.
5. Gitops. GitOps is an infrastructure and application management approach that uses Git as the only source of trust. It involves using version control to manage infrastructure changes and deployments.
6. Safety. Security is a critical aspect of DevOps, and teams work to ensure security at every stage of the software development life cycle. The process includes conducting security testing, implementing security controls, and continually monitoring vulnerabilities.
7. Service. Maintenance of software and infrastructure is an ongoing process in DevOps. Teams work to ensure that their products are durable, fault-tolerant, scalable, and easy to maintain over time.

With a focus on delivering infrastructure, automation, CI/CD, monitoring, GitOps, security, and maintenance, DevOps teams can quickly and efficiently build high-quality software while ensuring their products’ reliability, resiliency, and scalability.

However, other concepts such as DevSecOps and MLOps also support these methodologies and deserve our attention, and we will discuss them further.

The difference between classic DevOps, DevSecOps, DataOps, and MLOps

We have already discussed classic DevOps and discovered its methods and concepts. DevSecOps, DataOps, and MLOps are all variations of the DevOps methodology, with their focus and goals. Let’s look at each to understand which approaches are more relevant for specific tasks.

DevSecOps

DevSecOps stands for “Development, Security, and Operations” and refers to integrating development, security, and operations processes to ensure safe and efficient software deployment. The main goal of DevSecOps is to integrate security controls into every phase of the software development life cycle, from planning to deployment and support. It helps reduce security risks and ensures high-quality software.

Security personnel use DevSecOps tools to bridge the gap between development, deployment operations, and security issues. Integrating security practices into workflows helps to produce a secure product right from the start. DevSecOps differs from DevOps both in approach and tools. The main goal of DevSecOps is to prevent the occurrence of vulnerabilities in the project.

DevSecOps includes:

• SAST – tools for static testing of application security in integrated environments;
• CSPM – cloud security posture management.

But in general, this methodology adheres to the basic rules of DevOps and aims to speed up all the processes.

DataOps

DataOps is a methodology aimed at optimizing the processes of working with data, including their collection, processing, storage, and analysis. It focuses on automating and standardizing data processes and ensuring data security and consistency.

DataOps is closely related to DevOps and DevSecOps because all three methodologies focus on automating and standardizing software development and operations processes. DataOps focuses on working with and managing data. You can use it in DevOps and DevSecOps to improve data collection, analysis, and security throughout the application lifecycle.

DataOps extends DevOps practices to data analytics, shifting the overall responsibility of the team to engineers and administrators who collect and analyze, deliver, and protect data.

Implementing DataOps requires a change in mindset and workflow, just like any methodology. But at the same time, DataOps requires infrastructure modification: new patterns that focus on feedback and process automation.

DataOps can guide teams to implement next-generation technologies. This method helps to expand the scaling options significantly.

DataOps team members use tools like Spark and Hadoop, log aggregators Splunk or Sumo Logic, and monitor processes with Prometheus or Jira.

Overall, DataOps helps mitigate the risks associated with data security and consistency, which is especially important for companies dealing with sensitive data.

In the context of DevOps and DevSecOps, DataOps allows one to optimize collecting, processing, and analyzing data, ultimately improving software development quality and speed.

MLOps

MLOps is perhaps the most exciting and promising offshoot of DevOps. The thing is that specialists apply the general principles of DevOps to machine learning operations.

MLOps includes best practices, tools, and processes that enable organizations to develop, deploy, and scale machine learning models with high levels of automation and control.

The main goals of MLOps are to improve the performance and quality of machine learning models, accelerate their deployment in a production environment, and ensure the safety and reliability of the models. MLOps also focuses on tracking and quality control of the data which the models are trained on.

In many ways, MLOps overlaps with DataOps, as it also works with large data arrays. However, there are also some differences:

1. MLOps teams should include data scientists as well as ML researchers. These employees aren’t always software engineers; they focus on experiments more.
2. While classic DevOps follows a more linear approach, machine learning requires experimentation, which means that MLOps specialists must manipulate parameters and functions. Additionally, teams often need to retrain models, which adds to the complexity of the process. MLOps requires complex feedback loops and constant process reproducibility monitoring to ensure accuracy and consistency of the model.
3. Testing in MLOps is unique and requires additional methods not found in DevOps and DevSecOps.
4. When deploying an ML model, you often need data processing and training pipelines.
5. In production, models often encounter unorthodox challenges. For example, they may be associated with evolving data profiles that can lead to security degradation and model decay. Therefore, MLOps implies constant monitoring and auditing.

Otherwise, MLOps combines many tools and practices that engineers use in DevOps, including infrastructure and model deployment automation, CI/CD processes, code and configuration versioning, monitoring, and logging. MLOps also includes specific tools such as model versioning systems, data versioning, tools for developing and debugging models, and tools for managing and controlling the resources needed to train and deploy models.

MLOps is an essential methodology for developing and deploying machine learning models as it enables effective development and operation management. Furthermore, it helps improve the quality and speed of the models.

So, we briefly discussed all the methods that exist for now. It seems quite complex, doesn’t it? Let’s talk about DevOps professionals and try to understand how vital and challenging their work is and how companies evaluate it.

Is DevOps currently under-appreciated while being more stressful than other IT roles?

DevOps is an essential and complex role requiring a unique set of skills and deep knowledge of development and operations. DevOps professionals are responsible for creating and maintaining tools and processes that enable development teams to quickly and reliably build, test, and deploy applications.

One of the primary sources of stress in DevOps is the need to be available and quickly respond to any issues that arise with critical systems or applications. Engineers often need to be in touch and ready to act at any moment, regardless of the time of day or night. DevOps teams may be responsible for troubleshooting and resolving complex issues that are difficult to diagnose, which can increase workload and stress.

Therefore, many companies see DevOps as a valuable role. DevOps professionals have the opportunity to work on meaningful projects and often have a significant degree of autonomy and responsibility. In addition, DevOps is a fast-growing field with a high demand for skilled professionals, which can lead to attractive salaries and career opportunities.

Ultimately, the stress level in DevOps (or any job) will vary by person, company, and industry. But still, this is one of the most stressful roles in IT.

Regarding salary, DevOps is among the highest-paid professionals in the tech industry. According to PayScale, the average salary for a DevOps engineer in the US is around $102,000 annually.

In general, the work of DevOps is paid 25-30% better than the work of a software engineer. Let’s take a look:

Despite receiving high salaries, some people may feel that DevOps professionals’ compensation isn’t commensurate with their level of responsibility. DevOps engineers are critical in ensuring software is developed and deployed efficiently, reliably, and securely. It requires a broad range of skills and expertise, which can be challenging to acquire and maintain. DevOps engineers must frequently enhance their skills and keep up with recent technologies and practices.

However, many companies are still reluctant to provide adequate compensation to their DevOps professionals, which can lead to talented specialists leaving their jobs. It’s especially true in today’s competitive job market, where skilled professionals are in high demand.

However, it’s worth noting that salary levels can vary greatly depending on the individual, company, and industry. In some cases, DevOps positions may be paid less than other IT professionals with similar experience and responsibility. The specific labor market, industry trends, and the general state of the economy can influence wage levels.

The work of DevOps is so complex that many professionals are looking for ways to make it easier. One of the ways to simplify tasks is artificial intelligence. Yes, even in DevOps, new technologies can help. Let’s talk about using AI further on.

How AI assistants boost the productivity of DevOps

DevOps teams can utilize AI to enhance workflows. There are several ways:

1. Automated testing. DevOps professionals can use AI tools to automate testing processes. Artificial intelligence is good at finding bugs and problem areas in the code, which will speed up the development process.
2. Analytics. AI can analyze all deployments and find patterns that you can use to predict potential problems.
3. Monitoring. AI, unlike humans, can monitor processes around the clock. Therefore, the team doesn’t need to spend time tracking; AI will warn specialists of possible problems.
4. Intelligent automation. The team can outsource all repetitive tasks and processes to artificial intelligence. AI will allow specialists to free up time for more critical operations.

AI is already starting to transform DevOps and help teams be more productive and efficient. But to implement AI, you need to take several actions:

1. Determine what needs to be improved. It’s necessary to analyze all DevOps process aspects to understand which areas of artificial intelligence will be most justified.
2. Evaluate AI solutions. You need to study AI-based tools and understand how much they cost and how they scale. It’s essential to know how simple the solutions are and how compatible they’re with current processes.
3. Choose a solution and train the team. To implement an AI solution suitable for the price and requirements, you must explain to specialists how to integrate it into the workflow.
4. Rate performance. After introducing AI technology, it’s imperative to monitor how it works. Use analytics to change the strategy in time, if necessary.
5. Improve the process. Be open to new technologies and AI solutions to expand their use.

However, despite the many benefits, there are challenges to using AI that teams need to address. It’s necessary to pay attention to the quality of the data on which artificial intelligence systems are trained. It’s essential to assess the potential for errors as AI is still imperfect. AI solutions are also expensive, so you should choose them carefully.

The value of AI solutions cannot be overestimated. But can an experienced DevOps start a simple project without developers using AI assistants and infrastructure tools?

Yes, it’s possible. However, it’s important to note that the complexity of the project and specific tools will play a significant role in this question.

For example, suppose a project involves setting up a simple website or deploying a basic application. In that case, a DevOps professional with experience in infrastructure tools and automation can complete the project without the involvement of developers. They can use AI-powered tools to automate tasks such as provisioning servers, setting up databases and security protocols.

But for more complex projects that require custom software development or integration with other APIs, the DevOps engineer may need to involve developers to ensure the project is completed. In these cases, AI assistants and infrastructure tools can still be used to streamline the development and deployment process, but they will work alongside developers rather than replace them.

DevOps specialists can independently solve various problems, but what options are available for further career development?

Why does DevOps background work best on the way to the position of a system architect?

DevOps experience can be beneficial on the path to becoming a systems architect for several reasons:

1. Broad knowledge base: DevOps professionals typically have experience working across various technologies, platforms, and systems. This broad knowledge base can be valuable when transitioning to the role of a system architect, as it enables them to understand the complexities and dependencies of different systems and how they can be integrated.
2. Automation and optimization skills: DevOps professionals are skilled in automating and optimizing the development and deployment process. As system architects, they can apply these skills to design and optimize the overall architecture of a system, ensuring it’s scalable, efficient, and cost-effective.
3. Collaboration and communication: It’s vital in DevOps and system architecture roles. DevOps experts collaborate closely with developers, operations teams, and other stakeholders to guarantee a system’s seamless operation. Similarly, system architects must collaborate with various stakeholders to design and implement a system that meets the organization’s needs.
4. Continuous improvement: DevOps is built around continuous improvement, with regular iterations and feedback loops. This mindset can be valuable in a system architecture role, where continuous improvement is also vital to ensure the system remains relevant and meets the organization’s evolving needs.

As such, DevOps experience can provide a valuable foundation for a systems architecture role, with skills in automation, optimization, collaboration, communication, and continuous improvement especially useful.

Conclusion

So, we have learned that DevOps is a methodology that unites development and operations teams to expedite product delivery to the market and enhance their quality. DevOps has become a critical practice in the IT industry in recent years and has led to the emergence of new tools and approaches.

DevOps isn’t a separate role within a development team but a cultural approach that brings developers, testers, administrators, and other team members together. In today’s world, DevOps plays a significant role and can be seen as a set of practices and tools to speed up the process of getting products to market and ensuring their quality.

If you need the services of DevOps specialists, the Vilmate team is always ready to help. Our DevOps engineers are seasoned professionals who are well-versed in advanced development approaches.