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MLOps vs. DevOps: Why data makes it different

What is MLOps?

ML Ops refers to a set of strategies used by data scientists to develop and maintain machine learning models in the real world in an effective and reliable manner. Before releasing any algorithm, it is tested by data scientists, DevOps, and Machine Learning engineers to ensure that it is ready for production.

If we had to draw a diagram, the relationship between MLOps and DevOps would look like this:

MLOps attempts to build a coherent system in which tasks such as data ingestion, assessment, deployment, model training, and so on are performed in unison. Without them, data scientists would have to perform everything manually, such as cleansing data, selecting appropriate models, and operating the whole infrastructure.

MLOps are quite similar to DevOps in a lot of aspects, conceptually. But once you dig deeper, you would realize the huge amount of differences as well. Before proceeding with the differences, let us learn a little about DevOps.

What is DevOps?

DevOps is a set of practices in the world of software engineering that helps in the development and operation of large-scale software systems. With the help of DevOps, it is now possible to convert any software to production within a span of a few minutes.

MLOps vs DevOps

These are the major differences between MLOps and DevOps:


ML Ops are far more exploratory in nature than DevOps. This is due to the fact that with machine learning, developers have the opportunity to experiment and test various techniques to determine which ones perform best.

Traditional software engineering methods, such as DevOps, are likewise experimental, but they are not fully integrated into the primary project. Typically, the software is built independently and then linked to the production model after transformation.

Involvement of Data

One of the most significant distinctions between traditional software and machine learning is that, although software development is only concerned with code, ML also incorporates data in addition to coding.

Any Machine Learning model is created by running an algorithm on a large quantity of information. As you are aware, data originates from the actual world and is always changing. Code and data are two distinct entities that are difficult to reconcile. The role of ‘Data Engineering' enters the picture, assisting in the resolution of any ML-related production issues.

ML pipelines

Now that we have already mentioned Data Engineering, Let's talk about their requirements in ML. Data pipelines, which are a sequence of transformations that the data travels through between its source and finishing point, are a fundamental notion in data engineering.

Similarly, ML models frequently need some type of data transformation. These are controlled via data pipelines, which provide several benefits such as run-time visibility, code reuse, administration, and scalability. By including a few ML stages into the data pipeline, it is transformed into an ML pipeline. Because ML pipelines are simply based on code and are not dependent on data, they may be handled using a standard CI/CD pipeline, which is a basic DevOps technique.

Testing models

Any model must be tested before it can be deployed. Automation is tested in DevOps using unit tests and integration. Traditional software is easier to evaluate for model validation since it frequently produces correct and calculated results. ML models, on the other hand, are more difficult to evaluate because they do not offer a hundred per cent correct results.

As a result, the nature of the tests must be statistical in character. In the case of DevOps, the outcomes are binary, either pass or fail. As a result, in the case of ML models, one must investigate the metrics and determine the acceptable values for model validation.


Before putting any program to production, it is critical to collect monitoring data. Data handlers monitor standard metrics like latency, traffic, errors, and so on in order to gain control over any software's architecture.

Monitoring ML systems is tough since they rely on data that cannot be controlled or altered. As a result, model prediction performance is assessed in ML models alongside other parameters.

Another issue that arises while monitoring ML models is that there are no past models to compare them to because these models operate with new data every time.

Data validation

Any data pipeline is considered reliable when the input data is validated. These validations include file formatting, file size, column types, null or invalid values, etc. These are important factors to check in order to run the model smoothly.

Higher-level statistical characteristics of the input should also be validated using ML processes. This is because if the average or standard deviation of a feature changes significantly from one testing data to the next, the trained model and its predictions will most likely be affected.

Hybrid teams

ML Ops is operated with the help of Data engineering, DevOps engineering, and ML engineering. A data scientist alone would not be able to meet the requirements that would be necessary for ML Ops.

Thus the team handling MLOps practices would be needed to have knowledge of all the three and would be called an ML Ops Engineer.

Model and data versioning

Consistent version tracking is crucial for repeatability. In a conventional software environment, versioning code is sufficient since it defines all behaviour.

In ML, we must additionally keep track of model versions, as well as the data required to train them and certain meta-information such as training hyperparameters. Models and metadata can be kept in a normal version control system such as Git, but data is frequently too big and changeable for this to be efficient or practical.

It is also critical to avoid coupling the model lifetime to the code lifecycle because model training frequently occurs on a separate timetable. It is also important to version data and associate each trained model with the precise versions of code, data, and hyperparameters utilized.


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