Differences Between DevOps and MLOps

Nearly everyone in the IT industry is familiar with the term "machine learning," but it is no longer just a catchphrase used in spectacular presentations. The industry has started to include machine learning in significant projects as it has started to become more practical and less theoretical.

By 2022, we've already established its worth. How to successfully build a machine learning project and securely launch it into production is now the main concern.

Because MLOps is closely related to DevOps, folks who have worked on conventional software projects may be familiar with the phrase. To further understand how we can develop a consistent workflow that engineers and data scientists can iterate on for machine learning projects, let's look more closely at these words and their relationships.


DevOps is a method where individuals collaborate in a team to create and distribute software as quickly as possible. Software development and operations teams may produce software more quickly by working together and iteratively, thanks to DevOps. The DevOps model enhances communication between your project's developers and operations personnel.


We learned that DevOps was used to speed up the creation of software before it was deployed and monitored. Machine Learning Operations are the main topic of MLOps. So, data scientists, IT professionals, and DevOps engineers are the guys who are participating in this process. It is a helpful strategy for developing industry-leading machine learning solutions for users.

DevOps and MLOps advancement

In each concept, "development" has two distinct meanings.

You'll likely have code that generates some form of application or interface on the traditional DevOps side. The code is subsequently contained in an executable (artifact), which is then released and put to use before being checked against a set of tests. The ideal version of this cycle is automated, and it goes on until you get the finished item.

MLOps, on the other hand, uses code to create/train a machine learning model. Here, the output artifact is a serialized file that may accept input data and provide conclusions. The trained model's performance versus test data would be evaluated during validation. Similar to the previous cycle, this one keeps going until the model's performance reaches a particular level.

Version management in DevOps and MLOps

Tracking changes to code and artifacts is often all that version control in a DevOps pipeline entails. An MLOps pipeline has greater tracking requirements.

As previously indicated, model construction and training entail an iterative cycle of experimentation. To accurately recreate an experimental run later on for auditing purposes, its components and metrics must be tracked. The data set utilized for training (train/test split), the model construction code, and the model artifact are some examples of these components. The hyper-parameters and model performance are included in the metrics (e.g., error rate).

This could seem like a lot of information to track compared to conventional software solutions. We are fortunate to have model registry tools that are a perfect fit for versioning ML models.

DevOps and MLOps Monitoring

Model drift should also be monitored in MLOps in addition to the application itself. Since the data is always changing, your model must as well. In particular, if the data includes seasonality, models trained on earlier data may not perform well on future data.

Your model will require regular retraining in order to remain current and provide you with consistent value.

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