MLOps vs. DevOps: Key Differences and Similarities

Automation and operational efficiency are more than competitive advantages—they’re business necessities. As organizations increasingly embed artificial intelligence (AI) and machine learning (ML) into digital products and services, traditional DevOps methodologies are being stretched to accommodate the unique challenges of managing ML workflows. This is where MLOps comes into play.

MLOps, short for Machine Learning Operations, applies  DevOps principles to machine learning workloads to streamline the development, deployment, and monitoring of models at scale. Understanding the core similarities and differences between DevOps and MLOps is necessary for teams seeking to effectively operationalize software and AI initiatives.

What is DevOps?

DevOps is a set of software engineering practices designed to unify software development (Dev) and IT operations (Ops). It fosters a culture of collaboration, continuous improvement, and shared accountability while enabling faster, more reliable application delivery.

DevOps enhances the software lifecycle through foundational components such as:

• Continuous Integration and Continuous Deployment (CI/CD): Automates code integration and deployment, reducing the time between development and release.
• Infrastructure as Code (IaC): Enables teams to define and manage infrastructure through code, ensuring environment consistency and scalability.
• Monitoring and Logging: Offers real-time visibility into application health and performance for proactive issue resolution.
• Automation: Minimizes manual work across the SDLC, driving efficiency and reducing human error.

The benefits of DevOps include accelerated release cycles, improved collaboration across cross-functional teams, and more stable production environments. DevOps allows teams to innovate quickly without sacrificing reliability by streamlining development and operational workflows.

What is MLOps?

MLOps adapts and extends DevOps principles to meet the demands of machine learning systems. Unlike traditional software engineering, ML development involves unique complexities around data, model training, experimentation, and post-deployment behavior.

Key components of MLOps include:

• Experiment Tracking: During training, tracks which training code version and datasets were used to ensure model reproducibility and auditability. 
• Model Versioning: In addition to versioning training and inference code, models need to be versioned independently; a model may be updated without any changes to inference code, or vice-versa
• Model Evaluation: In addition to the standard tests done during the DevOps deployment process, models also need to evaluate their performance and accuracy prior to being deployed; this is usually done earlier in the process than other testing like unit, functional, or integration testing
• Model Deployment:. Traditional DevOps IAC and CICD practices are applied here to deploy models the same way as your other code deployments.  
• Monitoring: In addition to traditional monitoring and logging, model performance and data drift need to be monitored as well, which typically involves specialized tools, as well as periodic statistical analyses of incoming data and your model’s predictions. You may also need to consider a Ground Truth workflow to review whether model predictions were accurate. 
• Retraining: Retraining can be done manually, or triggered on an automated schedule when monitoring solutions detect changes in data or model performance, or when new data becomes available. 

The result is a more predictable, secure, and scalable approach to machine learning in production environments. MLOps enables organizations to reduce time-to-value for AI initiatives, improve model reliability, and ensure alignment with governance and compliance standards. The most effective implementations follow established MLOps best practices that support scalable, production-ready ML systems.

Key Differences Between DevOps and MLOps

While DevOps and MLOps share a foundational mindset—automation, agility, and collaboration—their implementations diverge in several critical ways, mainly due to the unique demands of machine learning development compared to traditional software engineering.

Software vs. ML Model Lifecycle

DevOps primarily builds and maintains software applications and manages source code and application binaries. Its workflows are typically deterministic. Code goes through testing, staging, and deployment phases. MLOps, by contrast, must account for the whole machine learning lifecycle, which includes not only code but also datasets, training pipelines, model artifacts, and evaluation processes.

Data is a first-class citizen in ML workflows, and its quality and variability directly impact outcomes. Even small shifts in data distribution, known as data drift, can significantly degrade a model's performance. MLOps, therefore, requires teams to manage datasets, training code versions, model versions lineage, and validations just as rigorously as they manage code.

CI/CD vs. CI/CD/CT

DevOps pipelines are built around Continuous Integration and Continuous Deployment (CI/CD). This model ensures code changes are tested and delivered quickly and reliably. These same principles are applied when deploying ML inference workloads. In addition to Continuous Integration and Deployment, MLOps also t adds Continuous Training (CT) to also automate and standardize model training and retraining. .

With CT, models are retrained automatically based on new incoming data, shifts in incoming data, or shifts in business requirements. This feedback loop is critical to maintaining relevance and accuracy in real-world use cases. A CT pipeline may seamlessly integrate tools like Amazon SageMaker Pipelines, Kubeflow Pipelines, or AWS Step Functions to orchestrate retraining, validation, and deployment.

Version Control Complexity

In DevOps, version control typically involves source code, environment configurations, and infrastructure templates. In MLOps, versioning becomes much more intricate. Teams must track:

• Code for data preprocessing and model training
• Training datasets and features
• Model weights and artifacts
• Hyperparameters and training configurations
• Evaluation metrics and testing conditions

Tools like DVC (Data Version Control), MLflow, and Amazon SageMaker have emerged to manage this complexity. Without rigorous version control, reproducing experiments or troubleshooting degraded model performance becomes nearly impossible.

Testing and Monitoring Scope

DevOps emphasizes functional, performance, and integration testing to ensure software behaves as expected. MLOps requires those layers plus additional focus on:

• Model accuracy and validation (e.g., precision, recall, F1 score)
• Bias and fairness assessments
• Data drift detection 
• Explainability and transparency in predictions

Monitoring tools in MLOps must assess not just system health, but also model efficacy over time. Frameworks like SageMaker Model Monitoring, WhyLabs, and Arize AI are commonly used to monitor inference quality and trigger alerts when anomalies are detected.

Tooling and Infrastructure

DevOps relies on established tools like Jenkins for automation, Docker for containerization, and Terraform or CloudFormation for infrastructure management. MLOps stacks introduce additional layers focused on data science needs:

• Experiment tracking: MLflow, Weights & Biases
• Model orchestration: Kubeflow, Metaflow
• Feature stores: Tecton, Feast
• Model Monitoring: WhyLabs, Arize
• Cloud-native infrastructure platforms: Amazon SageMaker, Vertex AI

These tools must integrate with DevOps systems while accommodating the complexity of ML development. Successful MLOps requires tight integration across software and data layers to ensure seamless productionization.

Similarities Between DevOps and MLOps

Despite their unique requirements, DevOps and MLOps are aligned in their core philosophies—both aim to accelerate delivery, reduce manual overhead, and foster collaboration across disciplines.

Collaboration and Automation

Collaboration is key in both practices. DevOps brings developers and IT operations together to work toward shared goals, while MLOps extends this collaboration to include data scientists, ML engineers, and business analysts. Automation plays a central role in reducing manual steps, whether it’s code testing or model retraining.

Organizations that embrace shared responsibility models see faster iteration cycles, fewer handoff delays, and improved team alignment. Communication tools like Slack, Jira, and Confluence are often paired with GitOps-style workflows to enforce transparency and traceability.

Scalability & Infrastructure as Code (IaC)

Infrastructure as Code (IaC) supports both disciplines by allowing infrastructure configurations to be version-controlled, tested, and deployed automatically. IaC reduces the risk of environment drift and ensures repeatable deployments across dev, test, and prod.

Tools like Terraform, AWS CloudFormation, and Pulumi are commonly used to provision standard application environments and machine learning infrastructure such as GPU clusters for training or SageMaker endpoints for inference.

CI/CD Pipelines

The backbone of both DevOps and MLOps is the CI/CD pipeline. While the contents of each stage may differ, MLOps requires data validation and model performance checks, the principle of automated, testable, and repeatable deployment remains consistent.

CI/CD pipelines are essential for accelerating time-to-market while maintaining stability and security. They also reduce the risk of human error, enabling teams to iterate faster and confidently deploy.

Security and Compliance

Security and compliance are integral to DevOps and MLOps, especially in regulated industries. Role-based access control, encryption in transit and at rest, audit logging, and adherence to frameworks like SOC 2, HIPAA, and GDPR are non-negotiable.

In MLOps, model governance becomes part of the compliance equation. Tracking which dataset and code version produced a given model is essential for audits and regulatory reporting. As AI-driven decisions become more prevalent, organizations must ensure models are performant, explainable, and accountable.

Best Practices for Implementing DevOps and MLOps Successfully

Adopting DevOps or MLOps isn’t just about tools—it’s about transforming how teams collaborate, automate, and deliver value. While each organization’s implementation will differ based on their tech stack and maturity, the following best practices are essential for building scalable, secure, high-performing pipelines.

Automating Workflows for Efficiency

Automation reduces human error, shortens feedback loops, and ensures environmental consistency. In DevOps, this includes automated testing, CI/CD pipelines, infrastructure provisioning, and monitoring. In MLOps, automation extends to model training, validation, deployment, monitoring, and retraining processes.

For example, you can configure pipelines to automatically trigger retraining when a new dataset arrives or model performance dips below a defined threshold. This closed-loop automation reduces time-to-resolution and ensures models remain accurate in production. Toolchains like GitHub Actions, SageMaker Pipelines, and Terraform can help orchestrate these workflows end-to-end.

Foster Cross-Functional Collaboration

DevOps brought together developers and IT operations—MLOps adds data scientists and machine learning engineers into the mix. Success depends on breaking down silos between these groups and fostering shared ownership over infrastructure, models, and application behavior.

Establishing clear communication protocols, shared documentation, and cross-functional retrospectives can help unify these teams. Using version-controlled notebooks, reproducible pipelines, and ticketing tools like Slack or Jira ensures alignment and traceability throughout the development lifecycle.

Build Reliable CI/CD Pipelines

CI/CD pipelines are the backbone of any modern software delivery process. In DevOps, this means automating testing, builds, and deployments. In MLOps, these pipelines also need to include steps like feature engineering, model evaluation, and performance validation.

Pipelines should include quality gates to prevent the promotion of unvalidated code or underperforming models. For MLOps, this might mean blocking deployment if a model’s accuracy falls below a certain threshold or if bias metrics exceed acceptable limits. Include rollback strategies for both software and models so that teams can quickly revert if issues are detected in production.

Implementing Robust Monitoring and Maintenance

Proactive monitoring is essential to maintaining performance and reliability. In DevOps, this includes metrics like server uptime, latency, and error rates. In MLOps, teams must monitor additional layers: prediction accuracy, data drift, inference latency, and user feedback.

Feedback loops, automated or human-in-the-loop, allow teams to act on performance issues early. While tools like Prometheus, Grafana, Datadog, and Amazon CloudWatch can be used to capture metrics, ML monitoring platforms like Arize, WhyLabs, or SageMaker Model Monitor can be used for ML-specific monitoring and feedback loops to track production behavior and trigger alerts when anomalies arise.

Embrace Comprehensive Versioning

Reproducibility is a cornerstone of trustworthy DevOps and MLOps workflows. In addition to source code and infrastructure, MLOps teams must version data, model artifacts, configurations, and even random seeds used during training.

By capturing lineage across the entire ML lifecycle, organizations ensure transparency, auditability, and collaboration. Solutions like Git, DVC, and MLflow allow teams to roll back to previous states, compare experiments, and meet compliance requirements.

Securing Systems and Ensuring Compliance

Security should be integrated into workflows from the ground up. In DevOps, this includes managing secrets, enforcing access control, and scanning dependencies for vulnerabilities. In MLOps, data security, model governance, and ethical AI usage come into play.

Establish secure data handling procedures, limit access to training and production environments, and ensure all changes are auditable. For highly regulated industries, consider embedding compliance checks directly into CI/CD pipelines to flag violations before models or software go live.

Master MLOps and DevOps with Mission

Mission helps organizations simplify and scale the complexities of modern software and AI operations. As an AWS Premier Tier Services Partner, we combine deep cloud expertise with proven DevOps and MLOps strategies to accelerate innovation, reduce risk, and maximize ROI.

Whether you're deploying your first machine learning models or managing enterprise-grade applications in production, our engineers and cloud architects work alongside your team to:

• Design and implement scalable CI/CD pipelines
• Automate model training, validation, and deployment workflows
• Modernize cloud infrastructure using Infrastructure as Code (IaC)
• Integrate real-time monitoring for applications and models
• Embed security, governance, and compliance at every stage

Partner with Mission to operationalize DevOps and MLOps strategies that accelerate delivery, enhance reliability, and align with your cloud architecture goals. Get in touch with one of our experts today.