DevOps vs DevSecOps vs MLOps – Which career path to choose in 2025?

In the dynamic technology landscape of 2025, IT leaders – from Chief Technology Officers (CTOs) to Program Managers and Team Leaders – face a key challenge: how to build and develop teams that not only deliver software quickly, but do so safely and can effectively implement data-driven innovations such as machine learning. The terms DevOps, DevSecOps and MLOps are no longer mere buzzwords; they have become strategic pillars of modern IT organizations, determining their ability to compete and adapt. Understanding the fundamental differences, competency requirements and market trends associated with these roles is essential for making informed investment decisions in talent development, process optimization and risk management. This article provides a comparative analysis of these three paths, targeting the needs of IT decision makers, managers and HR and Procurement professionals who are shaping the future of technology teams.

Understanding the essence of each of these methodologies is the foundation for strategic planning of team structure and resource allocation. Although they are all based on collaboration and automation, their strategic goals and responsibilities differ significantly, which has direct implications for the organization. From a management perspective, DevOps is primarily a strategy for accelerating the delivery of business value. The implementation of DevOps culture and practices, such as continuous integration and continuous delivery (CI/CD), test automation or Infrastructure as Code, aims to shorten the software development life cycle (SDLC), increase deployment frequency and improve system stability. For CTOs and Program Managers, this means faster time-to-market (Time-to-Market) and greater flexibility in responding to dynamic market changes. The key measure of success here becomes the throughput of the delivery process and its reliability.

DevSecOps, on the other hand, represents the evolution of DevOps, where security ceases to be treated as a separate stage and becomes an integral part of the entire process. For decision makers, DevSecOps is a risk management mechanism built into the software development process. In the face of growing cyber threats and increasingly stringent regulatory (compliance) requirements, integrating security at every stage of the SDLC, according to the “Shift Left Security” principle, is no longer an option, but a strategic necessity. Investing in DevSecOps competencies and tools minimizes the risk of costly data breaches, potential financial penalties and reputation damage that is difficult to rebuild. For the Chief Procurement Officer, strong DevSecOps practices are also becoming an important criterion for assessing the maturity and reliability of IT service providers.

Finally, MLOps, or Machine Learning Operations, is a relatively new but rapidly gaining discipline as a strategy for operationalizing and scaling artificial intelligence and machine learning initiatives. Many organizations are investing in Data Science teams, but without effective MLOps practices, promising ML model prototypes rarely turn into reliable, production solutions that deliver measurable business value. MLOps adapts DevOps principles to the specific lifecycle challenges of ML models, covering training data management, model versioning, training automation, deployment, monitoring model performance in production, and ensuring scalability. For CTOs, MLOps is the key to maximizing return on investment (ROI) in AI/ML and building a sustainable data-driven competitive advantage.

Why are these roles crucial in the digital transformation of 2025?

Digital transformation in 2025 is not just about deploying new technologies, but more importantly about building organizational capabilities for continuous adaptation, innovation and effective risk management. DevOps, DevSecOps and MLOps specialists are the architects and guardians of these capabilities, playing a fundamental role in achieving strategic business goals.

First, speed and operational agility, the domain of DevOps, remain key to staying competitive. Shorter deployment cycles, achieved through automation and improved collaboration between development and operations teams, allow organizations to respond faster to customer needs and dynamic market conditions. For any CTO and Program Manager striving to deliver ambitious product roadmaps on time, effective DevOps practices are absolutely essential.

Second, in a world where cyber attacks are becoming increasingly common and sophisticated, an organization’ s digital resilience is as important as its ability to innovate. This is where DevSecOps plays a key role, ensuring that the speed of software development is not achieved at the expense of security. Integrating security mechanisms throughout the application lifecycle minimizes the risk of incidents that could expose the company to financial and reputational damage. For decision makers, including Purchasing Directors assessing vendor risk, strong DevSecOps practices are an indicator of operational maturity and the ability to protect critical information assets.

Third, the effective use of data and artificial intelligence is becoming a powerful new field to compete for competitive advantage. MLOps is an essential component for turning the analytical potential of machine learning models into working, scalable and reliable production applications. Without robust MLOps practices, many ambitious AI strategies remain only on paper or in the pilot phase. Investing in MLOps competencies therefore becomes a strategic priority for companies looking to realistically reap the benefits of the AI revolution.

Finally, it should not be forgotten that all these roles, through automation, optimization and standardization of processes, contribute to operational efficiency and cost optimization. Automating tasks as part of DevOps and DevSecOps reduces labor intensity and human error. Efficient infrastructure management, often in cloud environments, and lifecycle optimization of ML models within MLOps allow better control of IT spending. This is a key aspect from the perspective of both the CTO and the Chief Procurement Officer seeking to maximize the value derived from technology investments.

Strategic Importance of Roles in Transformation 2025

✓ DevOps: Provides the foundation for rapidly delivering business value and achieving operational agility, key to competitiveness.
✓ DevSecOps: Strengthens an organization’s digital resilience, proactively manages risk and ensures regulatory (compliance) compliance.
✓ MLOps: Is key to successfully operationalizing AI/ML, maximizing data ROI and building analytics advantage.
✓ Common denominator: All of these roles support the strategic goals of digital transformation, contributing to an organization’s efficiency, innovation and security.

What technology trends are driving the demand for DevOps, DevSecOps and MLOps professionals?

Understanding key technology trends allows IT leaders to anticipate future competency needs and shape team development strategies accordingly. The demand for DevOps, DevSecOps and MLOps specialists is not an accidental phenomenon – it is a direct response to the evolution of technology, IT systems architecture and the changing business and regulatory environment.

One of the most important driving forces is the dominance of Cloud-Native architecture. The migration to the public cloud (such as AWS, Azure and GCP) and the widespread use of container technologies (Docker) and orchestration systems (primarily Kubernetes) have become the de facto standard for modern applications. Managing this growing complexity, automating deployments in dynamic cloud environments, optimizing costs (FinOps) and ensuring security at multiple levels requires advanced DevOps and DevSecOps skills. For CTOs and Architects, this means a fundamental shift in the way IT systems are designed, built and operated.

Another important trend is the establishment of Infrastructure as Code (IaC) as a standard practice. Defining and managing the entire infrastructure – servers, networks, databases, security policies – through code (using tools such as Terraform, Ansible or Pulumi) is key to achieving automated, repeatable and auditable IT environments. This is a core competency required of modern operations and platform teams, enabling rapid and consistent implementation of infrastructure changes while minimizing the risk of errors.

The data explosion and democratization of artificial intelligence and machine learning cannot be ignored. The exponential growth in the amount of available data, coupled with increasingly easy access to advanced AI/ML tools, is causing companies around the world to intensively explore ways to use it to optimize processes, create new products or personalize services. This phenomenon is directly driving a gigantic demand for MLOps specialists who possess a unique combination of skills to build and maintain reliable, scalable data pipelines and ML models in production settings.

At the same time, cyber security has become an absolute business imperative. The growing number, scale and sophistication of cyber attacks make the traditional reactive approach to security, often treated as a separate step at the end of the development process, far from sufficient. The need to integrate security at every stage of the software development lifecycle (DevSecOps) is becoming a standard required not only by best practices, but increasingly also by regulations, corporate customer requirements and internal risk management policies. This trend is rapidly elevating the demand for DevSecOps experts capable of implementing a “Shift Left Security” culture and tools.

Finally, we are seeing the rise of Platform Engineering as an evolution of the DevOps approach in large organizations. In response to the growing complexity of tools and processes, companies are creating dedicated platform teams to build and maintain Internal Developer Platforms (IDPs). The goal is to provide developers with self-service tools and automated workflows that simplify the process of developing, testing and deploying software, while ensuring consistency, security and compliance with standards. Building and managing such platforms requires advanced DevOps and DevSecOps competencies.

These trends clearly indicate that investment in developing competencies in the areas of DevOps, DevSecOps and MLOps is not a fad, but a strategic necessity for organizations wishing to effectively leverage the potential of modern technologies and remain competitive. Acquiring and retaining specialists with these skills is becoming a priority for HR departments and a key challenge for Procurement Directors negotiating contracts with external IT service providers, such as ARDURA Consulting, who can provide access to the needed expertise.

DevOps vs DevSecOps: where does automation end and security as a process foundation begin?

For technical leaders and program managers, it’s crucial to understand that the transition from DevOps to DevSecOps is not just about adding new tools to the pipeline, but more importantly about fundamentally changing the organizational culture and redefining responsibilities within the team. It’s about consciously moving away from a model where security is treated as a separate silo, often operating reactively and at late stages of a project, to one where it becomes an integral, proactive part of the daily work of every team member involved in the value delivery process.

In the traditional DevOps approach, the main focus is on optimizing the workflow – from the moment the developer writes the code to its deployment to the production environment. Automation focuses on streamlining the build, test (mainly for functionality and integration) and deployment processes, which is done through a CI/CD pipeline. The overarching goal is to maximize the speed and reliability of delivering new business functionality. In such a model, security issues often arise late in the cycle, for example, in the form of penetration testing performed just before deployment, or are delegated to a separate security team. Such separation can lead to inefficiencies, project delays (when security issues are detected too late) or, worse, the implementation of solutions with significant risks.

DevSecOps is revolutionizing this approach by consistently implementing the “Shift Left Security” principle. This concept involves proactively incorporating security issues, practices and tools as early as possible in the software development lifecycle. This means that security is taken into account as early as the system architecture design stage, for example through Threat Modeling. During the coding process, developers are supported by automated tools for static code analysis (SAST) and dependency and open-source component analysis (SCA), which help catch potential vulnerabilities on the fly before they reach the repository. In addition to functional testing, the automated CI/CD pipeline also runs dynamic application security testing (DAST) and scans container images for known vulnerabilities. Security of the infrastructure configuration, defined as code (IaC Security), is also verified automatically. What’s more, security monitoring doesn’t end with the deployment – it’s an ongoing process, covering both running applications and the entire infrastructure in the production environment.

From the perspective of a Technical Team Leader, implementing DevSecOps means that the responsibility for developing secure software is shared by the entire team – developers, testers, operations engineers and security specialists. This requires not only a change in mentality (“security is everyone’s job”), but also the acquisition of new skills and the effective use of appropriate tools. For the Program Manager, this means integrating security tasks and requirements into project plans from the very beginning, rather than treating them as an additional burden at the end. For the CTO and the organization as a whole, DevSecOps is a strategic investment in building long-term stability, system resilience and customer confidence.

DevOps vs DevSecOps: The Key Difference for Leaders.

✓ DevOps approach: Focus on maximizing speed and efficiency of delivery of business functionality. Security often treated as a separate control step at the end of the process.
✓ DevSecOps approach: Focus on achieving secure speed of delivery. Security is a fundamental element integrated throughout the SDLC cycle, from design to production (“Shift Left”).
✓ Strategic Implications: DevSecOps requires a profound change in organizational culture, promotes shared responsibility for security, forces the implementation of new tools and competency development, and above all, enables proactive risk management from the very beginning of the project.

MLOps – why is it a “bridge” between AI potential and real business value?

Many organizations, recognizing the revolutionary potential of artificial intelligence, are investing significant resources in building Data Science teams. These teams, made up of highly skilled professionals, create advanced machine learning (ML) models capable of solving complex business problems. However, the road from creating a working prototype of a model in a lab environment to its reliable, scalable and efficient deployment in production is often long and challenging. Without an effective mechanism to operationalize, monitor and maintain ML models in the real world, many promising AI/ML initiatives remain at the stage of expensive experiments, failing to deliver the expected business value. It is this critical gap that MLOps fill, acting as a strategic “bridge” linking the potential inherent in algorithms to real, measurable benefits for organizations.

The problem that MLOps addresses is fundamental and stems from the peculiarities of the life cycle of machine learning models, which differs significantly from the life cycle of traditional software. ML models are not static – their performance can degrade over time in response to changes in input data (a phenomenon known as model drift or data drift). They require continuous monitoring not only from a technical point of view (availability, latency), but more importantly from the point of view of prediction quality and impact on business metrics. It is also necessary to have a mechanism for their systematic retraining on new data to maintain their relevancy. In addition, the process of developing and deploying ML models involves managing complex dependencies between source code, huge sets of training data, versions of the models themselves, and environment configuration. Traditional DevOps practices, while a good foundation, are not fully sufficient to address these unique challenges.

MLOps adapts and extends principles and tools known from DevOps to the specific needs of the machine learning world. It brings engineering discipline and automation to the entire process, which includes, among other things: strict versioning not only of the code, but also of the training data and the trained models themselves, ensuring repeatable experiments and auditable results; creation of automated ML pipelines, which include all stages from data preparation and validation, through training and evaluation of the model, to its implementation in production; implementation of mechanisms Continuous Training (CT). i Continuous Delivery (CD) for ML models, allowing the training process to be automatically triggered in response to new data or code changes, and to seamlessly deploy new and better versions of models; Advanced monitoring of model performance in a production environment, tracking both technical and business metrics, and automatically detecting said data or concept drift; and effective management of computing infrastructure (often using specialized hardware like GPUs and TPUs) needed to train and serve models, often in scalable cloud environments.

For CTOs and business leaders, investing in building MLOps competencies and processes is a key step toward de-risking AI/ML projects and ensuring they deliver a measurable return on investment. For the Program Manager, MLOps provides the structure and tools to manage ML projects in a more predictable and controlled manner. For the Technical Team Leader, MLOps brings the necessary engineering discipline to the process of working with models, facilitating collaboration between Data Scientists and engineers. When sourcing in-house MLOps experts is difficult due to the newness of the field and high demand, working with experienced external partners such as ARDURA Consulting, which specializes in building and optimizing MLOps processes, becomes a strategic choice for companies looking to compete effectively based on analytics and artificial intelligence.

What are the career paths and advancement opportunities in each of these specialties from a team-building perspective?

Understanding the typical career paths in the rapidly growing fields of DevOps, DevSecOps and MLOps is crucial for Team Leaders, HR Partners and Technology Directors who are responsible for strategic talent planning, internal development program design and effective external recruitment. While each of these specialties offers unique opportunities for growth and advancement, they also place specific demands on candidates and organizations to acquire and develop competencies.

A development path in DevOps is often a natural evolution for experienced Linux/Windows system administrators or developers with a jam for automation and infrastructure. The initial stages of a career focus on mastering key tools and technologies, such as continuous integration and delivery systems (e.g. Jenkins, GitLab CI, Azure DevOps), containerization (Docker) and orchestration (Kubernetes) technologies, and infrastructure-as-code management tools (e.g. Terraform, Ansible). As experience is gained, the development of a DevOps specialist leads to the design and implementation of complex, scalable and reliable cloud architectures, optimization of system performance and cost, and often specialization in Site Reliability Engineering (SRE), which focuses on ensuring high availability and reliability of services. Opportunities for advancement in this field are broad and include positions such as DevOps/Cloud Architect, Senior SRE Engineer, and then leadership roles such as Platform Engineering Team Leader or even Head of Platform Engineering/Head of DevOps. From an organization’s perspective, building a strong DevOps team is an investment in a solid foundation to deliver software efficiently and quickly.

The DevSecOps development path is often seen as a specialization for more experienced DevOps engineers or IT security experts who want to bridge the two worlds and implement a security culture in development processes. This role requires a unique combination of deep technical knowledge of automation, infrastructure and cloud with a solid understanding of application, data and infrastructure security. The development of the DevSecOps specialist focuses on gaining hands-on skills in implementing and managing security tools built into the CI/CD pipeline (SAST, DAST, SCA, vulnerability scanners), applying threat modeling techniques, automating security controls and configuration, and implementing security policies as code (Security as Code). Advancement opportunities in this area lead to positions such as Security Architect (with a focus on cloud and applications), DevSecOps Team Leader, and in some organizations can be an important step on the path to the role of Chief Information Security Officer (CISO). For a company, investing in developing DevSecOps competencies is building a critical technology risk management and compliance function.

The development path in MLOps is currently the most interdisciplinary and rapidly evolving. It attracts people with diverse backgrounds – most often Data Scientists looking to operationalize their models, Data Engineers building data pipelines for ML, or Software Engineers/DevOps with a strong interest in the area of artificial intelligence. The development of an MLOps specialist involves mastering a specific ecosystem of tools (such as MLflow, Kubeflow, DVC, ML platforms offered by cloud providers like AWS SageMaker, Azure ML or Google Vertex AI), honing programming skills (mainly in Python and its libraries), and understanding the entire ML model lifecycle – from experimentation to production and monitoring. While it is not required to be an algorithm expert, a solid ML foundation is essential. Opportunities for advancement include positions such as Senior MLOps Engineer, ML Platform Architect, and in the future, more specialized leadership roles responsible for the strategy and implementation of AI in an organization are likely to emerge. Due to the relative newness of the field, career paths are more flexible, creating unique opportunities to shape one’s own role, but also challenging organizations to accurately plan for talent development and retention.

For the HR Technology Partner, understanding these diverse pathways is key to creating accurate job descriptions, effectively sourcing candidates and designing development programs. For the Team Leader, it is the foundation for effectively mentoring and supporting team members in acquiring new, strategically important competencies. For the CTO, it is the foundation for consciously building diverse and complementary technology teams capable of meeting the challenges of the future.

Which skills are essential for DevOps and which for DevSecOps and MLOps – an organizational requirements perspective?

Accurately defining the competency requirements for key technology roles is the foundation of an effective recruiting process, workforce development planning and assessing the potential of teams. While there is some common technology core for DevOps, DevSecOps and MLOps professionals, each of these roles requires a unique set of skills that directly translate into specific operational and strategic capabilities for the organization.

From an organizational perspective, the core competencies of a DevOps team must ensure the ability to deliver software quickly, reliably and efficiently. This requires, first and foremost, proficiency in process automation, which includes a mastery of CI/CD tools (such as Jenkins, GitLab CI, Azure DevOps, GitHub Actions), the ability to create automation scripts in languages such as Python, Bash or PowerShell, and a working knowledge of configuration and infrastructure management tools as code (especially Terraform and Ansible). A deep understanding and ability to manage cloud and container infrastructure is also essential. This means a working knowledge of the services of key cloud providers (AWS, Azure, GCP) and proficiency in the Docker and Kubernetes ecosystem, which are the foundation of modern, scalable applications. Equally important is the ability to implement effective monitoring and observability, which requires the ability to configure and use tools such as Prometheus, Grafana, ELK Stack or commercial platforms (e.g., Datadog, Dynatrace) to proactively manage performance, system availability and quickly diagnose problems. Finally, analytical skills, systems thinking and the ability to solve complex problems in distributed environments are key.

The DevSecOps team must have all of the above DevOps competencies, enhanced with deep security knowledge and skills. First and foremost, this means a solid understanding of application security (AppSec) principles, including knowledge of common attack vectors (e.g., OWASP Top 10), secure coding practices, and system and network hardening methods. A working knowledge of and ability to integrate security tools directly into the development pipeline – static code analysis tools (SAST), dynamic application analysis (DAST), software component analysis (SCA), as well as vulnerability scanners and Web Application Firewall (WAF) tools – is crucial. Experience in securing cloud and container environments is also required, which includes configuring cloud-specific mechanisms (e.g., IAM, security groups, key management) and implementing security best practices for Kubernetes (e.g., network policies, image scanning, RBAC). In addition, DevSecOps professionals should have knowledge of risk and compliance management, including familiarity with relevant standards (e.g. ISO 27001, SOC 2, PCI DSS) and the ability to implement controls to ensure compliance.

The MLOps team needs a unique combination of skills at the intersection of software engineering, Data Science and IT operations. The foundation is strong software engineering in an ML context, which means programming proficiency (primarily in Python and its ecosystem for working with data and models, such as Pandas, NumPy, Scikit-learn), knowledge of good engineering practices, and the ability to write production code. Hands-on experience with MLOps-specific platforms and tools, including ML platforms offered by major cloud providers (AWS SageMaker, Azure Machine Learning, Google AI Platform/Vertex AI) and popular open-source tools for data and model versioning (e.g., DVC, MLflow), ML pipeline orchestration (e.g., Kubeflow, Airflow, Argo Workflows) and model serving (e.g., KFServing/KServe, Seldon Core, FastAPI) is essential. A solid foundation in data engineering is also required, including the ability to work with a variety of data sources, build effective data processing pipelines (ETL/ELT), and knowledge of databases (SQL and NoSQL). An in-depth understanding of the entire ML model lifecycle is also key, including the processes of training, validation, deployment, monitoring and retraining, as well as an awareness of issues such as model drift. Finally, many core DevOps competencies are directly transferable and essential to MLOps, particularly containerization (Docker), CI/CD practices, infrastructure-as-code (IaC) management and monitoring skills.

Accurately mapping these competency requirements is crucial for HR Partners in the talent acquisition process and for Team Leaders in planning training and development paths. Often, finding candidates with the full required profile is challenging, prompting organizations to seek support from external technology partners, such as ARDURA Consulting, who have teams with the needed expertise.

Mapping Key Competencies for Teams

✓ DevOps team: Must have the ability to build and manage automated CI/CD processes, effectively operate cloud and container infrastructure, and ensure observability of systems. The goal is speed, reliability and efficiency of software delivery.
✓ DevSecOps team: Extends DevOps competency with deep knowledge and practical skills in application security, infrastructure, cloud and Sec tool integration across the SDLC cycle. The goal is to achieve secure velocity and proactive risk management.
✓ MLOps Team: Requires a unique combination of strong software engineering, hands-on knowledge of the ML ecosystem and MLOps platforms, data engineering fundamentals, and an understanding of the specifics of the ML model lifecycle, supported by DevOps competencies. The goal is to effectively and reliably operationalize AI/ML initiatives.

Salaries in 2025: which path implies the greatest investment in talent?

Analyzing salary trends in key technology specializations is an essential part of strategic planning for IT decision makers. Understanding which roles require the greatest investment in talent acquisition and retention allows for realistic budgeting, effective management of team costs, and informed design of recruitment and retention strategies. While exact salary levels always depend on a number of factors, such as geographic location, company size and industry, candidate experience level or certifications, the overall market trends for 2025 paint a clear picture.

It should be noted that all three of the specializations discussed – DevOps, DevSecOps and MLOps – consistently rank among the top highest-paid roles in the entire IT industry. This is a direct result of their strategic importance to digital transformation, their documented impact on business performance, and the continued high demand while there is a relative shortage of qualified experts in the labor market.

DevOps engineers have maintained a very strong position in the salary market for years. Their key role in accelerating software delivery cycles, optimizing infrastructure and ensuring system stability is widely recognized. Experienced DevOps specialists, especially those with proven skills in advanced management of cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes), can expect very competitive financial terms. From an organization’s budgetary perspective, this is a significant but already well-established and predictable investment in the fundamental operational efficiency of IT departments.

DevSecOps specialists, due to the critical importance of security in today’s world and the lower availability of experts who can seamlessly combine competencies from Development, Security and Operations, can often expect salaries that rank above those offered at equivalent levels of experience in “standard” DevOps. Companies are willing to pay a significant premium for expertise that directly translates into reducing business risk, protecting against costly security incidents and ensuring regulatory compliance. For the Chief Procurement Officer negotiating vendor contracts and the HR Partner planning recruitment budgets, this means taking into account the potentially higher costs of acquiring and retaining talent in this specialty.

MLOps engineers, representing the newest and one of the fastest growing fields, are also at the very top of the IT salary ladder. The demand for experts who can effectively build, implement and manage machine learning models at production scale is growing exponentially and significantly outstrips the current supply in the labor market. As a result, the salaries offered to MLOps specialists often match, and in some cases may even exceed, the rates seen in DevSecOps, especially in companies and industries investing heavily in the development and deployment of artificial intelligence-based solutions. Investing in MLOps talent is seen as a strategic expenditure to build a key capability to effectively leverage data and AI.

To summarize the implications for decision makers in terms of planning for 2025: expect DevSecOps and MLOps roles to require the greatest investment in salaries. This is due to the unique combination of required skills, business criticality (risk management and AI deployment), and still limited talent supply. At the same time, investment in high-level DevOps professionals will remain crucial and costly, although the market in this area is somewhat more mature. These trends need to be taken into account by CTOs, HR and Procurement Departments when planning budgets for salaries, retention programs, recruitment and potential collaboration with external technology partners who can offer flexible access to the needed highly specialized competencies.

How to choose between DevSecOps and MLOps, having experience in Data Science – a talent development perspective?

The decision to specialize, especially for those who already have a solid foundation in Data Science, is important not only for an individual career path, but also from the perspective of an organization planning to develop the competencies of its team. The choice between orienting development toward DevSecOps or MLOps has strategic implications, as it shapes the team’s ability to deal with different types of technological and business challenges.

For a professional with experience in Data Science, the MLOps path often seems a more natural continuation. Having an understanding of data, machine learning algorithms and the model development process is an invaluable asset in the role of MLOps Engineer. This track allows you to directly apply and deepen the knowledge you have gained, focusing on a key challenge: how to effectively move ML models from the experimental phase to a stable, scalable production environment. From the perspective of the Team Leader and CTO, developing Data Scientists into MLOps means building internal capabilities for the full lifecycle of AI/ML projects, from concept to maintenance. However, this requires the employee to invest in learning software engineering principles, DevOps tools (such as CI/CD, containerization), and specific MLOps platforms and tools, an area where mentoring support or external training may be essential.

On the other hand, the choice of a DevSecOps path by someone with a background in Data Science means a more significant shift in focus. Instead of focusing on data and models, the professional directs his or her attention to software development processes, infrastructure management and, most importantly, ensuring the security of IT systems. While it may seem less intuitive, the analytical skills and systems thinking developed in Data Science are extremely valuable in the security field – for example, in log analysis, anomaly detection, threat modeling or risk assessment. Experience working with data can also be directly useful in the context of data protection (Data Security) and regulatory compliance (e.g., GDPR/RODO). For an organization, having someone with a deep understanding of data on the DevSecOps team can bring a unique perspective. However, this path requires the candidate to acquire extensive knowledge of application security, infrastructure, cloud, and mastery of DevOps tools and practices, which represents a significant investment of time and effort in learning new areas.

Thus, from a strategic team development perspective, encouraging Data Scientists to develop into MLOps seems a more direct way to strengthen an organization’s ability to deploy AI. Encouraging DevSecOps exploration, on the other hand, may be of value to those individuals who have a strong interest in cybersecurity and want to apply their analytical skills in a new context, potentially creating unique interdisciplinary roles within the organization.

Will DevOps be replaced by DevSecOps in an era of cyber threats?

The question of the future of DevOps in the context of the growing dominance of DevSecOps is often raised by IT leaders planning long-term development strategies for their teams and processes. Should we consider DevSecOps as a complete successor to DevOps? A more accurate answer is to say that DevSecOps is not so much a replacement for DevOps as a natural, mature evolution of it, which is becoming the new standard in responsible software development.

The foundations laid by DevOps – a culture of collaboration, process automation (CI/CD, IaC), continuous monitoring and the drive to shorten the delivery cycle – remain absolutely key and invariably important. DevSecOps does not negate these principles; on the contrary, it builds on them, expanding on them by deeply integrating security as a fundamental aspect of quality at every stage of the process, rather than as a separate, often delayed control.

In today’s business reality, where the consequences of a successful cyber attack can be catastrophic both financially and reputationally, and regulatory requirements for data protection and system security are increasingly stringent, ignoring security aspects in the software development process is simply unacceptable. Therefore, it can be argued that “done right” DevOps in 2025 and beyond is de facto DevSecOps. Organizations that strive for operational excellence, risk management and building customer trust must naturally evolve to fully integrate security practices into their DevOps processes.

Of course, in practice, many companies are still at different stages of this evolution. Some may continue to operate in a model closer to traditional DevOps, gradually implementing individual security elements. But the long-term strategic goal for any mature IT organization should be to reach a state where security is an integral part of the culture and daily practices of development and operations teams. Instead of thinking about replacement, IT leaders should view DevSecOps as a necessary standard and the next higher level of maturity for all DevOps initiatives. Companies that ignore this evolution will face increasing operational and strategic risks, potentially losing their competitive edge to those that treat security as an integral part of value delivery.

How do certifications (e.g., AWS, Kubernetes, CISSP) affect a candidate’s value from the organization’s perspective?

In the process of recruiting and assessing the competence of IT professionals, technology certifications play an important, albeit complex, role. For decision-makers – Team Leaders, HR Partners, as well as Purchasing Directors evaluating the qualifications of vendor specialists – understanding the real value of certifications is key to making sound personnel and business decisions.

Undoubtedly, certifications issued by reputable organizations (such as cloud providers – AWS, Microsoft Azure, Google Cloud; standardization organizations – Linux Foundation/Cloud Native Computing Foundation for Kubernetes; or security-focused institutions – (ISC)² for CISSP, ISACA for CISM) provide objective confirmation of a candidate’s possession of specific theoretical and often practical knowledge in a given, well-defined field. In the preselection process, especially with a large number of applications, certifications can serve as an important filter, signaling a potential level of competence. For the HR Partner, this is often the first indicator of a candidate’s fit with the technical requirements of the position.

What’s more, the very process of preparing for the certification exam forces the candidate to study in a structured manner and systematically assimilate a wide range of knowledge, often including industry best practices, technological nuances and usage scenarios that may not have come up in their previous work experience. This demonstrates the candidate’s commitment to professional development and the drive to maintain up-to-date knowledge in a rapidly changing technology industry.

In a competitive recruiting environment, possession of relevant, recognized certifications (e.g. AWS Certified DevOps Engineer – Professional, Certified Kubernetes Administrator (CKA), Certified Information Systems Security Professional (CISSP) for DevSecOps roles) can set a candidate apart from others of similar seniority, providing an additional argument in their favor. In some cases, especially in large corporations, the public sector or in partnership programs with technology providers, possession of certain certifications may even be a formal requirement for a position or a condition for participation in prestigious projects.

However, it is crucial to understand that certifications by themselves are no substitute for practical experience and the ability to solve real-world problems. The most valuable professionals for an organization are those who can effectively combine theoretical knowledge, confirmed by certification, with the ability to apply it practically in complex, real-world project scenarios. Therefore, in the recruitment process, in addition to verification of certifications, it is essential to thoroughly test the candidate’s practical skills through technical tasks, behavioral interviews or analysis of a portfolio of completed projects. For a Purchasing Director evaluating bids from IT service providers, the team’s certifications may be an indicator of quality, but references, case studies and the provider’s work methodology are equally important.

In summary, certifications are a valuable part of an IT professional’s profile, enhancing their credibility and value in the 2025 job market. They send an important signal to organizations, but should be treated as a complement, not a substitute, for proven practical experience and real-world problem-solving skills.

How is artificial intelligence changing DevOps and DevSecOps tools?

The impact of artificial intelligence (AI) and machine learning (ML) on the IT industry goes far beyond the creation of new products and services. These technologies are also beginning to revolutionize the very tools and processes used by DevOps and DevSecOps teams, leading to a concept known as AIOps (AI for IT Operations), which is also increasingly boldly entering the security field. For IT leaders, understanding the potential of AIOps is key to planning the future evolution of tools and processes in their organizations, seeking to further automate, increase efficiency and proactively manage increasingly complex systems.

One of the most promising application areas for AI in DevOps/DevSecOps is intelligent monitoring and analysis of operational data. Traditional monitoring systems generate huge amounts of data – metrics, logs, traces (traces) – which manual analysis is time-consuming and often inefficient. AI/ML algorithms are used to automatically detect anomalies in system behavior, identify complex patterns and correlate events from different sources (e.g., correlating a performance problem with a specific deployment or configuration change). What’s more, AIOps makes it possible to anticipate potential problems (predictive analytics) before they have a chance to affect end users, allowing for proactive preventive action. For operations teams, this means a significant reduction in the time needed to diagnose and resolve incidents (MTTD – Mean Time To Detect, MTTR – Mean Time To Repair).

Going a step further, AIOps enables not only intelligent problem detection, but also incident response automation. For repetitive, well-defined types of failures, AIOps systems can automatically run predefined recovery procedures (known as runbook automation), such as restarting services, scaling infrastructure resources or restoring the previous stable configuration. This allows even faster restoration of services and minimizes the impact of failures on the business.

Another important application is the optimization of resource utilization and costs, especially in cloud environments. ML algorithms can analyze historical and current resource utilization patterns (CPU, memory, network, disk space) and, based on this, recommend optimal sizes for VMs, containers or databases, and even automatically manage infrastructure scaling (autoscaling) more precisely than traditional rules. This leads to significant operational cost savings, which is crucial from the perspective of FinOps and CFOs.

AI is also used to streamline software testing processes. It can be used to automatically generate test cases, intelligently prioritize regression tests (focusing on the riskiest areas of an application), identify potential duplicates in test suites or even automate visual user interface tests.

In the context of DevSecOps, AI is playing an increasingly important role in strengthening security. ML algorithms are being used in modern threat detection and response systems (Security Information and Event Management – SIEM; Security Orchestration, Automation and Response – SOAR) to analyze huge volumes of security data, identify subtle patterns indicative of attack, reduce false positives and prioritize alerts that require analyst attention. AI can also help analyze vulnerabilities, predict which ones are most likely to be exploited by attackers, and automate responses to security incidents.

Although AIOps technology is still in the development stage and its implementation comes with challenges (e.g., data quality, need for expertise), its potential to further transform IT operations and security is enormous. For IT leaders, tracking the development of AIOps tools and strategically planning for their adoption is becoming an important part of building forward-thinking, highly automated and intelligent IT operations.

Cloud-native vs on-premise: which architecture dominates 2025 projects?

The decision on deployment architecture – whether to base systems on cloud infrastructure (cloud-native), keep them in their own data center (on-premise), or perhaps use a hybrid model – is one of the key strategic decisions facing CTOs and Architects in 2025. This choice has fundamental implications not only for cost and flexibility, but also for the required set of competencies in DevOps and DevSecOps teams and risk management strategies, which is important from the perspective of CTOs and compliance departments.

There is no doubt that cloud-native architectures, leveraging the full potential of services offered by public cloud providers (AWS, Azure, GCP), are gaining dominance, especially for new projects and applications requiring high scalability, global availability and rapid innovation cycles. The cloud offers unparalleled flexibility, a pay-as-you-go model, a rich ecosystem of managed services (databases, message queues, AI/ML tools) and the ability to rapidly deploy infrastructure as code. However, effectively managing complex cloud-native environments, optimizing costs (FinOps) and ensuring security in a dynamic, shared responsibility model requires advanced and constantly updated DevOps and DevSecOps skills. Teams must be fluent in the specifics of a given cloud platform, containerization technologies (Kubernetes) and microservices architectures.

On the other hand, on-premise infrastructure remains relevant and is often the preferred choice in certain scenarios, especially in highly regulated industries (e.g., finance, healthcare, public sector) where there are stringent requirements for data sovereignty, physical security or specific certifications. Organizations may also choose to maintain portions of on-premise systems due to existing infrastructure investments, specific performance requirements (e.g., low latency), or a desire to retain full control of the environment. Managing on-premise environments also requires strong DevOps/DevSecOps competencies, although the focus may be slightly different – more emphasis on managing hardware, virtualization, networking and physical security, as well as integration with potential cloud solutions in hybrid models.

We are also increasingly seeing the widespread use of hybrid architectures that seek to combine the advantages of both approaches, placing different system components in the cloud and on-premise depending on business, technical and regulatory requirements. Managing such distributed, heterogeneous environments presents additional challenges for DevOps and DevSecOps teams, requiring the ability to integrate, orchestrate and ensure consistent security across domains.

In summary, while the cloud-native trend is strong and will likely dominate new initiatives in 2025, the architecture decision must be made on a case-by-case basis, based on a strategic analysis of business needs, regulatory requirements and risk profile. Regardless of the model chosen, investing in the development of advanced DevOps and DevSecOps competencies is absolutely critical to ensure the efficiency, security and reliability of IT systems, and the complexity of managing cloud-native and hybrid environments further raises the bar of requirements for these teams.

From a Team Leader’s perspective, what challenges do newcomers face in each of these paths?

Bringing in new team members or supporting existing employees in their transition to DevOps, DevSecOps or MLOps roles is an important task for Technical Team Leaders and HR Partners. Understanding the typical challenges faced by newcomers to these specializations allows for better planning of the onboarding, mentoring and development process, which minimizes the risk of turnover and accelerates the achievement of full productivity.

For those embarking on the DevOps path, the main challenge is often the vastness and variety of tools and technologies that need to be mastered. The DevOps ecosystem is very dynamic – from CI/CD systems, to containerization and orchestration, IaC tools, cloud platforms, and monitoring and logging systems. Beginners may feel overwhelmed by having to learn so many different elements at once. Additionally, the DevOps role requires a broad, interdisciplinary view, combining knowledge of systems administration, networking, databases and programming fundamentals. Successful onboarding should therefore focus on a gradual introduction to successive areas, providing access to training materials and assigning a mentor to help navigate this complex world.

For DevSecOps, the challenges are even greater, because in addition to extensive DevOps knowledge comes the need for a deep understanding of IT security principles and practices. For someone transitioning from a purely development or operations role, the biggest barrier may be a change in mindset – learning to think about security every step of the way, proactively rather than reactively (“thinking like an attacker”). It is necessary to master specific security tools (SAST, DAST, SCA, etc.) and understand their place in the development process. Beginners may also struggle with interpreting the results of security scans and making decisions about prioritizing and remediating vulnerabilities found. Key here is support from experienced security professionals, access to AppSec and CloudSec training, and building a culture where asking questions about security is welcome.

Those entering MLOps face challenges due to the interdisciplinary nature of the role, combining the worlds of software engineering, Data Science and IT operations. For someone with a background in Data Science, a barrier may be the need to learn good engineering practices, DevOps tools and infrastructure management. For a software engineer/DevOps, on the other hand, it can be a challenge to understand the specifics of the ML model lifecycle, basic machine learning concepts and working with large data sets. Additionally, the MLOps tool ecosystem is still relatively new and rapidly evolving, requiring constant learning and adaptation. Effective onboarding in MLOps often requires creating an environment of close collaboration between Data Scientists and Engineers, providing access to the right platforms and tools, and investing in training that includes both disciplines.

In all three cases, in addition to technical challenges, newcomers often face difficulties related to the culture of collaboration and communication between different teams (Dev, Ops, Sec, Data Science), which is the foundation of these methodologies. The role of the Team Leader is to actively foster integration, build bridges and promote open communication.

DevSecOps in practice: how is “Shift Left Security” revolutionizing software development?

The “Shift Left Security” concept, at the heart of the DevSecOps methodology, represents a fundamental shift in the approach to security in the software development lifecycle (SDLC). Instead of treating security as a separate control phase performed at the end of the process (which often leads to delays, costly patches or risk acceptance), “Shift Left” is about proactively integrating security practices and tools as early as possible, making them an integral part of the daily work of development and operations teams. This revolution in approach brings tangible benefits to organizations, from reducing costs to accelerating secure deployments.

In practice, “Shift Left Security” manifests itself through the implementation of specific actions and tools at each stage of the SDLC:

1. Design and Architecture Phase: Security is considered from the very beginning. Techniques such as Threat Modeling are used to identify potential vulnerabilities and design defense mechanisms right at the system architecture level. Security requirements for new functionality are also defined.
2. Coding Phase: Developers are equipped with the tools and knowledge to write more secure code. This includes training in secure coding, the use of coding standards, and the integration of Static Analysis of Code (SAST) tools directly into development environments (IDEs) or as part of the code commit process. These tools automatically scan source code for known vulnerability patterns, providing developers with immediate feedback.
3. Build and Integration (CI) Phase: As part of the Continuous Integration pipeline, additional security checks are automatically run in addition to compilation and unit tests. These include Software Component Analysis (SCA), which scans the project’s dependencies (open-source libraries) for known vulnerabilities (CVEs), and a repeat, more in-depth SAST scan. The results of these scans can block the build process if critical issues are detected.
4. Testing Phase: In addition to functional and performance testing, security testing is also being automated. Dynamic Application Analysis Testing (DAST) tools are used to test a running application (running in a test or staging environment) for vulnerabilities, simulating external attacks. Interactive Application Security Testing (IAST), which combines the advantages of SAST and DAST, is also increasingly being used.
5. Deployment (CD) and Operations Phase: Before deployment to production, infrastructure configuration security (IaC Security) is verified using dedicated tools. Container images are scanned for vulnerabilities. After deployment, continuous security monitoring of the production environment is crucial, using Runtime Application Self-Protection (RASP) tools, intrusion detection systems (IDS/IPS) and advanced SIEM/SOAR platforms.

Implementing “Shift Left Security” is not just a matter of tools, but more importantly a cultural shift. It requires breaking down silos between development, operations and security teams, promoting shared responsibility and providing appropriate training and support. The role of Technical Leaders is to actively promote this culture and implement appropriate practices within their teams.

The benefits of this approach are significant: early detection and remediation of vulnerabilities is much cheaper and faster than fixing them at late stages or after deployment. Integrating security into an automated pipeline allows software delivery speeds to remain high without compromising security. As a result, organizations can build more resilient systems, minimize the risk of incidents and increase the trust of their customers.

Does MLOps require advanced knowledge of mathematics and statistics?

One of the common questions that arises in the context of building MLOps teams and defining requirements for candidates is the level of mathematical and statistical knowledge needed. Does an MLOps Engineer have to be an expert in these fields, just like a Data Scientist creating new algorithms? The answer is: not necessarily, although a solid foundation is definitely helpful.

A clear distinction should be made between the role of the Data Scientist, whose job is often deep data analysis, exploration, design and implementation of new machine learning models (which actually requires an advanced understanding of mathematics, statistics and algorithm theory), and that of the MLOps Engineer. The main task of the MLOps Engineer is to operationalize, automate and manage the lifecycle of ML models created by Data Scientists. The focus thus shifts from creating algorithms to engineering the processes involved in implementing, monitoring and maintaining them in a production environment.

Obviously, an MLOps Engineer must have a good conceptual understanding of the basics of machine learning. He or she should know what the different types of models are (e.g., regression, classification, neural networks), how the basic algorithms work, key model evaluation metrics (e.g., accuracy, precision, sensitivity, F1-score, AUC), and common problems such as overfitting or underfitting. He or she must also understand the importance of data quality, model validation processes, and phenomena such as data drift and concept drift in order to effectively design monitoring systems.

However, the MLOps Engineer is not required to be able to derive complex mathematical formulas, prove statistical theorems or design entirely new neural network architectures on his own. Its strength lies in its engineering skills: proficient programming (especially in Python), familiarity with MLOps tools and platforms, the ability to build automated pipelines, manage infrastructure (often cloud-based) and apply best practices known from DevOps to the ML context.

From the perspective of a Team Leader and HR Partner, this means that the barrier to entry into MLOps for those with a solid engineering background (e.g. Software Engineer, DevOps Engineer) may be lower than commonly thought. Of course, some level of “familiarity” with the mathematical and statistical concepts underlying ML is necessary to communicate effectively with Data Scientists and make informed engineering decisions, but it doesn’t have to be at an expert level. It is critical to find candidates with strong engineering skills, a passion for automation and a willingness to learn the specifics of the ML world. For Data Scientists moving into MLOps, the challenge will be to add to their engineering knowledge rather than deepen their math.

The future of the industry: which roles have the greatest potential for growth after 2025?

Looking ahead beyond the 2025 horizon, IT leaders need to anticipate which roles and competencies will be critical to maintaining their organization’s competitiveness and ability to innovate. An analysis of current technology and business trends helps to sketch a picture of the most promising growth paths in IT operations and software development.

All three specializations discussed – DevOps, DevSecOps and MLOps – will undoubtedly maintain their strategic importance and offer attractive growth opportunities. The foundations laid by DevOps in terms of automation and process efficiency will remain crucial. However, given the two megatrends shaping the future of technology – ubiquitous cyber threats and the explosion of artificial intelligence applications – it can be predicted that the roles associated with DevSecOps and MLOps will have the greatest potential for growth in demand.

Demand for DevSecOps specialists will continue to grow rapidly, driven by the need to build digital resilience, meet increasingly stringent regulatory requirements, and growing business awareness of the costs and risks associated with security incidents. Integrating security across the application lifecycle will become not just a best practice, but a standard expectation. Application, cloud and data security roles, woven into DevOps processes, will be absolutely critical.

At the same time, MLOps experts will become some of the most sought-after professionals in the market. As more companies move from experimenting with AI to deploying machine learning-based solutions at scale, the need for people who can build, manage and scale reliable ML pipelines will be huge. MLOps will become a key enabler of real business benefits from AI investments, and specialists in this field will play a central role in many organizations’ digital transformation strategies.

Also worth noting is the evolution of the DevOps role into Platform Engineering. As tools and processes become more complex, there is a growing need for in-house development platforms (IDPs) that abstract this complexity and provide developers with self-service tools to streamline their work. Platform Engineers with deep DevOps/DevSecOps knowledge and the ability to build such platforms will also be very valuable in the job market.

Regardless of the specific specialization, the ability to continuously learn, adapt to rapidly changing technologies, and strong soft skills such as communication, collaboration and problem solving will remain key to future career success in all of these roles. Organizations that invest in developing these competencies in their teams – whether through internal training or working with partners such as ARDURA Consulting – will be better prepared for the challenges and opportunities that the future of IT will bring.

The Future of IT Competencies Beyond 2025: Key Growth Directions

✓ DevSecOps: Surge in demand driven by security and compliance imperative. Sec integration in DevOps will become standard.
✓ MLOps: Demand explosion due to widespread AI/ML adoption. Key role in operationalizing and scaling ML models.
✓ Platform Engineering: DevOps evolution toward building internal development platforms (IDPs) for increased productivity and standardization.
✓ Common Requirements: Regardless of path, continuous learning, adaptability, cloud skills and soft skills will remain key.
✓ Strategy for Leaders: The need to proactively plan for talent development and build teams with the competencies of the future to maintain competitive advantage.