There’s no doubt that DevOps has been the inspiration behind MLOps.

MLOps inherits many of the principles and practices from DevOps, aiming to streamline development and deployment processes for machine learning applications, much like DevOps has done for software engineering.

By observing how rapidly software is built and deployed using DevOps, the machine learning community — which was struggling with deployment complexity — began to adopt DevOps methodologies.
However, while MLOps is fundamentally built on DevOps principles, it introduces additional complexity due to the nature of ML workflows.

Let’s dive into a detailed comparison of DevOps vs. MLOps, understanding where they align and, more importantly, where they differ.

1. Team Composition

In DevOps, teams are usually composed of:

• DevOps engineers

On the other hand, MLOps requires a more diverse team, including:

• Data scientists

• ML engineers

• Data engineers

• DevOps engineers

This diversity reflects the broader skill sets needed to manage the complexities of data pipelines, model training, and deployment in MLOps environments.

2. Development Process: Linear vs. Iterative

DevOps development tends to be linear:

• Once code is written and integrated, it usually moves straightforwardly to deployment.

In contrast, MLOps development is iterative and experimental:

• Data scientists constantly experiment with new data, features, models, and hyperparameters.

• Achieving maximum prediction accuracy involves complex feedback loops and multiple iterations.

3. Versioning: Code-Only vs. Code + Data + Features

In DevOps:

• Only the code needs to be versioned.

• A change in code produces a new application.

In MLOps:

• Multiple components need to be versioned:

  • Code

  • Data

  • Features

• Different combinations of these produce different model outputs, making comprehensive versioning essential.

4. Build Time and Hardware Requirements

DevOps builds are generally:

• Quick (minutes to an hour)

• CPU-powered (not compute-intensive)

MLOps builds, especially for deep learning models, are:

• Compute-intensive

• Require GPUs for accelerated training

• Can take hours to weeks for larger models

This hardware dependency significantly increases the complexity of ML build processes.

5. Automation of Training Runs

In DevOps:

• Automated builds are reasonable since build times are short.

In MLOps:

• Automatic training for every code change isn’t practical due to long training times.

• Teams implement staged builds and training runs to balance automation with practicality.

6. Continuous Integration (CI)

DevOps CI focuses on:

• Testing and validating code

MLOps CI extends further:

• Testing and validating not just code but also data:

  • Ensuring data distribution is appropriate

  • Detecting skewed data or outliers

  • Checking for null values

Data validation is crucial because poor-quality data directly impacts model performance.

7. Continuous Deployment (CD)

In DevOps:

• CD involves deploying a build package or service to a web server or API endpoint.

In MLOps:

• CD means deploying an entire continuous training pipeline:

  • On trigger, the pipeline retrains and redeploys the model automatically.

  • This adds significant complexity, requiring automation of steps that were traditionally manual.

8. Monitoring Intensity

DevOps monitoring typically includes:

• Throughput

• Latency

• CPU utilization

• System uptime

MLOps monitoring is far more intense, covering:

• Model accuracy

• Data drift

• Feature monitoring

• Latency and system metrics

• Ethical and regulatory compliance (e.g., bias detection)

MLOps demands vigilance to ensure:

• Models do not become biased (gender, race, etc.)

• Compliance with government audits, especially in sensitive industries like pharmaceuticals and finance

Failing to monitor these aspects can result in:

• Financial loss

• Loss of trust and reputation

• Legal consequences

In DevOps, software applications behave exactly as coded, making risks lower and more predictable.
In contrast, ML models are influenced by external factors like:

• Evolving data patterns

• Changes in customer behavior

This unpredictability makes MLOps inherently riskier and requires heightened monitoring.

9. Continuous Training (CT)

DevOps focuses on:

• Continuous integration and continuous delivery (CI/CD)

MLOps introduces an additional critical layer:

• Continuous training (CT)

Why?

• Models degrade over time due to changing data profiles.

• Even if rare, models can begin behaving abnormally right after deployment.

• New data patterns gradually reduce model performance.

To address this, MLOps integrates:

• Scheduled retraining

• On-demand retraining

• Trigger-based retraining (e.g., when new data arrives)

By continuously training models, MLOps ensures that models remain relevant and accurate, adapting to the latest data dynamics.

Conclusion

In summary, while MLOps is deeply inspired by DevOps, it extends its principles to meet the specific needs of machine learning systems.

MLOps isn’t just about delivering models to production — it’s about delivering trustworthy, adaptive, and compliant machine learning systems that evolve with data over time.

As organizations embrace MLOps, they gain the ability to deploy models faster, monitor them effectively, and retrain them continuously, keeping pace with the dynamic nature of real-world data.