MLOps vs DevOps: Differences and Similarities

While DevOps has been around for over a decade now and almost everyone is somewhat aware of what it is, a relatively new, similar sounding term is making the rounds. Yes, we are talking about MLOps. But what exactly are these two and how is MLOps different from DevOps?

In this blog, we are going to talk about MLOps vs DevOps, covering both differences and the similarities between them. Both these software development strategies incorporate collaboration between operations, data science, and developers. By earning a Devops or an MLOps certification, you will become an in-demand professional in the sector. Let’s get started!

What is MLOps?

So, what is MLOps?

MLOps refers to a suite of strategies used for automating the process of machine learning, connecting development, operations, and model creation. It incorporates DevOps principles with ML to ensure that ML projects go smoothly.

MLOps solutions aid in putting ML models into action quickly to deliver updates to the clients in shorter duration. A good MLOps training online is something that should not be ignored by someone who wishes to enjoy a bright career in this field.

MLOps Tools & Platforms

The top MLOps tools and platform used widely include –

  • Kubeflow
  • MLflow
  • TensorFlow Extended (TFX)
  • AWS SageMaker
  • Apache Airflow
  • Databricks

What is DevOps?

Every software company has two departments, namely development and operations. Traditionally, there were a lot of silos between these two, which had a negative impact on the overall software development lifecycle.

With DevOps, a lot of barriers have been broken, leading to better overall output. It is neither a tool nor a technology, but an approach that renders better agility in operations. You may go through this DevOps Tutorial to learn more about DevOps.

DevOps Tools & Platforms

The top DevOps tools and platform used widely include –

  • CI tools like CircleCI, Jenkins, etc.
  • CD tools like Chef, Ansible, etc.
  • Monitoring & logging tools like Logstash, Nagios, etc.
  • Source code management tools like Subversion, Git, etc.
  • Containerization tools like Kubernetes and Docker
  • Configuration management tools like Puppet, SaltStack, etc.

MLOps vs DevOps: The Similarities

When it comes to understanding MLOps and DevOps in light of their similarities, then here are a few points to keep in mind.

  • DevOps and MLOps best practices revolve around process automation in continuous development. The goal is to maximize efficiency, productivity, and speed.
  • DevOps and MLOps work by facilitating collaboration between different teams, such as data science, operations, and development teams.
  • A culture of experimentation is promoted by both to help teams test and validate new approaches and ideas quickly.

MLOps vs DevOps: The Differences

To help you completely understand the concept of MLOps vs DevOps, here is a table of distinction for you. 

Basis MLOps DevOps
Focus The main focus of MLOps is one ML operation and models. The main focus of DevOps is on IT operations and software development.
Key Components The key components of MLOps are model registries, monitoring, and data pipelines. The key components of DevOps are CI/CD pipeline, infrastructure, and repositories.
Objective  The main objective of MLOps is to enhance management and deployment of ML models. The main objective of DevOps is to accelerate reliability and delivery of software.
Teams Involved In MLOps, data analysts, IT ops, and data scientists are involved. In DevOps, development and operations departments collaborate.
Main Benefit It improves ML model reliability and efficiency. It enhances software development quality and speed.
Data Handling MLOps works with ML-specific models and data. DevOps manages data related to application and code.
Main Tools The main tools include TensorFlow Extended (TFX), MLflow, and Kubeflow. The main tools include Docker, Jenkins, Kubernetes, and GitLab.
Main Challenge The main challenges are model explain ability, model drift, and data bias. The main challenges are infrastructure management, and continuous integration.


This blog on MLOps vs DevOps is for anyone who is searching which one to pick as a career option. If you are questioning if an MLOps certification course is something you should go for, then the answer is simply a yes. Get trained by the best to explore unlimited future growth.

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