Differences Between MLOps and DevOps
Differences Between MLOps and DevOps
In this article, we are going to see about the comparison or Differences between MLOps and DevOps. Let's Get into the topic.



  • You may have heard a lot about machine learning in the IT world. As it offers the computer the ability to learn things on its own, without being taught, it is regarded to be one of the major applications of artificial intelligence. Humans have been shown to benefit from automation since they are relieved of the burden of having to code each new piece of data that has to be entered into the system.

  • Without relying on natural human intellect, machine learning can automate a variety of human-driven processes. Although there are numerous applications for machine learning, they can be broadly divided into two types: ML Operations and Dev Operations. There are significant variances between these two despite sharing identical underlying underpinnings.

  • Let's explore the definitions of ML Ops and Dev Ops in greater detail and contrast them.


What Is ML Ops?

  • A group of techniques known as "ML Ops" is employed by data scientists to efficiently and consistently create and maintain machine learning models in the real world. Data scientists, DevOps professionals, and machine learning engineers verify each algorithm before it is made public to make sure it is ready for use.

  • The goal of MLOps is to create a system that performs operations like data ingestion, evaluation, deployment, model training, and others in harmony. Without them, data scientists would have to carry out all tasks manually, including data cleansing, model selection, and infrastructure management.

  • MLOps and DevOps are conceptually quite similar in many ways. However, if you looked closer, you would also see a vast number of distinctions. Let's first gain some understanding of DevOps before moving on to the distinctions.


What is DevOps?

  • A set of procedures known as "DevOps" in the field of Software engineering aids in the creation and management of large-scale software systems. Any piece of software may now be quickly moved from development to production thanks to DevOps.

  • To ensure that the entire process flows smoothly, programming, testing, and operational aspects of software development are combined here. DevOps is concerned with two concepts: Continuous Integration and Continuous Delivery.

The source states that there are seven steps involved in putting any ML/DL model into production:

  • Data extraction

  • Data analysis

  • Data preparation

  • Model training

  • Model evaluation

  • Model evaluation

  • Model serving

  • Model monitoring

Now let's talk about the key distinctions between ML Ops and Dev Ops:


DevOps vs. MLOps


  • Compared to DevOps, ML Ops are significantly more exploratory in nature. This is because machine learning gives developers the chance to experiment and try different methods to see which ones work the best.

  • DevOps and other conventional methods of software engineering are experimental as well, but they are not fully incorporated into the main project. Typically, the software is created separately, transformed, and then connected to the production model.

Data Involvement:

  • Machine learning (ML) involves data in addition to coding, but traditional software development is just concerned with code. 

  • Any Machine Learning model is produced by executing an algorithm on a vast quantity of information. Data comes from the real world and is always changing, as you are aware. Data and code are two separate things that are challenging to combine. For the purpose of resolving any production-related ML concerns, the role of "Data Engineering" is introduced.

ML processes:

  • After mentioning data engineering, let's discuss how it applies to machine learning. A basic idea in data engineering is the concept of a data pipeline, which is a series of changes that the data goes through between its source and finishing point.

  • Similarly, data transformation is typically required for ML models. Data pipelines, which are used to govern these, have a number of advantages, including run-time visibility, code reuse, administration, and scalability. The data pipeline is converted into an ML pipeline by adding a few ML stages.

Evaluating models:

  • Any model must first undergo testing before being put into use. DevOps tests automation through integration and unit testing. Since traditional software typically generates accurate and calculated results, it is simpler to evaluate it for model validation. On the other hand, ML models are more challenging to assess because they do not always provide accurate results.

  • Therefore, the tests must be statistical in nature. DevOps results are binary; they are either pass or fail. Because of this, when it comes to ML models, one must look into the metrics and choose the appropriate values for model validation.


  • Gathering monitoring data is essential before launching any program. Data handlers keep an eye on common metrics like latency, traffic, errors, and so on to obtain control over the design of any product.

  • Since ML systems rely on uncontrollable and unchangeable data, monitoring them can be challenging. As a result, along with other parameters, model prediction performance in ML models is evaluated.

  • Since ML models use fresh data every time they run, there are no existing models to compare them to. This presents another problem when monitoring ML models.

Data Confirmation:

  • Any data pipeline that has validated its input data is regarded as dependable. These checks include things like column kinds, null or incorrect values, file formatting, file size, and more. To ensure that the model runs properly, it is crucial to verify these elements.

  • It is important to use ML techniques to validate the input's higher-level statistical properties. This is due to the fact that the trained model and its predictions will most likely be impacted if the average or standard deviation of a feature drastically varies from one set of testing data to the next.

Hybrid groups:

  • Data engineering, DevOps engineering, and ML engineering are used to operate ML Ops. The needs for ML Ops would be too demanding for a data scientist to handle alone.

  • As a result, the group in charge of MLOps procedures would need to be familiar with all three, and they would be known as ML Ops Engineers.


Data and model versioning:

  • For repeatability, consistent version tracking is essential. Code versioning is sufficient in a traditional software system since it defines all behavior.

  • Additionally, with ML, we need to keep track of model iterations, the data needed to train them, and certain meta-data like training hyperparameters. While data is frequently too massive and dynamic to be preserved in a standard version management system like Git, models, and metadata can.

  • Additionally, because model training usually happens on a different schedule, it is essential to avoid linking the model lifespan to the code lifecycle. Versioning data and tying each trained model to the exact versions of the code, data, and hyperparameters used are also crucial.

  • The ideal solution would be a custom tool, but there isn't one on the market right now. Instead, a variety of approaches are being used, most of which are based on file/object storage standards and metadata databases.



  • You have learned several facets of machine learning models in this blog as well as how they differ from conventional models created through software engineering. DevOps has less complexity than MLOps, but there are also fewer opportunities for novel techniques and advancement.

  • Since of these restrictions, MLOps now play a significant role in every software model because they allow data scientists to experiment. In its industry, ML has made significant strides and is now widely employed in business solutions.


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