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In recent years, machine learning has gone from an experimental activity to a strategic component of many digital products. However, bringing a machine learning model from the research phase to production is not trivial. It is precisely in this context that MLOps was born, a set of practices that extends the principles of DevOps to the world of machine learning.
What is MLOps
MLOps (Machine Learning Operations) is an approach that combines Machine Learning, Data Engineering, and Operations with the goal of:
- automating the lifecycle of ML models,
- ensuring reproducibility and quality,
- making the release and maintenance of models in production reliable.
A Machine Learning model is not just code: it depends on data, features, hyperparameters and the execution environment. MLOps was created to manage this complexity.
The MLOps lifecycle
A typical MLOps workflow includes:
- Data ingestion and versioning
- Training and experimentation
- Model validation
- Deployment in production
- Performance monitoring
- Continuous retraining
Each phase must be traceable, automated, and monitorable.
What is DevOps (in brief)
DevOps is a culture and a set of practices that aim to:
- reduce time to market,
- improve the quality of software,
- foster collaboration between development (Dev) and operations (Ops).
DevOps is based on key concepts such as:
- CI/CD (Continuous Integration / Continuous Delivery)
- Infrastructure as Code
- Automation
- Continuous Monitoring
The Relationship Between MLOps and DevOps
MLOps does not replace DevOps, but extends it to address the specific challenges of machine learning.
Commonal Points
MLOps inherits many core principles of DevOps:
| DevOps | MLOps |
|---|---|
| CI/CD | CI/CD for ML models |
| Automation | Automated training and deployment |
| Monitoring | Model and data monitoring |
| Code versioning | Code, data, and model versioning |
Both aim to reduce manual errors, Increase reliability and accelerate release.
Key Differences
The main difference lies in the nature of managed systems.
| DevOps | MLOps |
|---|---|
| Deterministic Code | Probabilistic Models |
| Relatively Stable Inputs | Data That Changes Over Time |
| Code Bugs | Data Drift and Model Drift |
| Traditional Testing | Statistical Validation |
A model can degrade even if the code doesn't change: this is called model drift, a problem typically addressed only by MLOps.
Why DevOps Isn't Enough for Machine Learning
Applying only DevOps practices to ML often leads to problems such as:
- non-reproducible models,
- lack of feature traceability,
- difficulty in debugging performance,
- absence of retraining automatic.
MLOps introduces specific tools and processes to fill these gaps, such as:
- dataset versioning,
- experiment tracking,
- automatic retraining pipeline,
- data distribution monitoring.
MLOps as a natural evolution of DevOps
We can see MLOps as a natural evolution of DevOps in a data-driven context.
If DevOps made software faster and more reliable, MLOps aims to do the same for intelligent systems.
In many mature organizations:
- DevOps manages applications and infrastructure,
- MLOps manages models, data, and learning pipelines.
The two disciplines work together to ensure that AI-based systems are scalable, reliable, and governable.
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Conclusion
MLOps is the answer to the operational complexity of machine learning in production.
It stems from DevOps principles, but expands them to include data, models, and continuous learning.
In an increasingly AI-driven world, DevOps and MLOps are not alternatives, but rather complementary disciplines, essential for building modern and sustainable solutions.
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