7 Key Differences Between MLOps and DevOps

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As organizations become data-driven, they continue to search for new ways to leverage data for better decision-making and outcomes. Most often, the primary focus of businesses is on artificial intelligence (AI) and machine learning (ML) and how to use these technologies to unlock valuable insights. Businesses today are measuring different approaches to deploy the algorithms that can power predictive analytics and roll them out efficiently.

In this regard, DevOps and MLOps are two approaches to software development that have garnered much attention in recent times. To speed up the process, work with an experienced software development company. Such a company can provide quality guidance on the best technologies and approaches for achieving targeted results.

Before you hire a service provider, put your best foot forward by learning the basics and key differences between MLOps and DevOps. It will help you compare and evaluate your options and proceed with the integration confidently.

Time to get started!

DevOps- What Is it?

DevOps is a traditional software development strategy. It is often described as the evolution of Agile methodology to software development. The approach combines the software development and operations teams into one unit.

As developers and engineers from both departments work cohesively, it ensures a smooth workflow from software planning to creation, deployment & operations. With DevOps, businesses can efficiently convert software programs to production in the shortest time. All elements of software development – designing, testing & operational are combined to ensure a coherent flow of the process. DevOps largely focuses on two basic concepts – Continuous delivery and continuous integration.

DevOps offers many benefits, such as it:

  • Helps test new code for quick deployment
  • Empowers companies to roll out faster updates
  • Helps secure a competitive edge in their industry

MLOps- What Is it?

On the other hand, MLOps is the abbreviation of Machine Learning DevOps. It is a DevOps specialized subset tailored to develop machine learning apps. Indeed, MLOps is not just a software development strategy but a cultural and technological shift. It needs the best people, tools, and processes for successful implementation. It includes a set of proven strategies for machine-learning lifecycle automation.

It combines the best strategies and practices of DevOps with machine learning to accelerate ML model deployment into production.

Among the top benefits of MLOps are that it helps organizations achieve long-term value while reducing data science, artificial intelligence, and machine learning risks. Furthermore, it helps:

  • Tap new streams of revenue
  • Save time
  • Reduce the cost of resources by employing data analytics, optimizing operations, and improving customer experiences.

Similarities Between MLOps and DevOps

MLOps and DevOps are similar to some extent. Both methodologies facilitate and encourage collaboration between:

  • Data scientists and software engineers
  • Individuals who manage infrastructure
  • Stakeholders

MLOps and DevOps emphasize on automation process in continuous development for maximum efficiency and speed.

Top 7 Differences Between MLOps and DevOps

Now that you have a basic understanding of the two approaches, here are the key differences:

1. Experimental

Compared to DevOps, MLOps is more exploratory. Machine learning empowers developers to test and experiment with different techniques to find the best one. ML engineers and data scientists tweak features like parameters, hyper-parameters, and models and manage the code base so that the results can be reproduced.

DevOps are somewhat experimental but aren’t included in the primary project. It means the software is independently built, and after transformation, it is connected to the production model.

2. Testing

Software testing services ensure the quality of the models and higher customer satisfaction. In DevOps, software testing is performed using unit and integration testing methods. Unit testing is a method in DevOps where a small piece of code is tested to assess if it works as intended.

This form of testing aims to lower the cost associated with fixing bugs and errors. After unit testing, the DevOps team performs integration testing to verify whether the two software units’ interfaces work efficiently. Put simply, DevOps focuses on software development tasks like code building and deployment.

On the other hand, MLOps is focused on automated ML (machine learning) tasks and collecting data insights for making decisions that impact business results positively. Hence, its testing process is not limited to unit or integration testing; it goes beyond that- including model training, testing, and validation. Models are trained to take the desired actions in a given situation for the best results.


3. Infrastructure

MLOps and DevOps rely on cloud technology. However, the operational requirements of both methodologies are different. DevOps depends on infrastructure like build servers, CD/CI automation tools, and infrastructure–as–code (IAC).

MLOps depends on the following infrastructure:

  • Machine and deep learning frameworks
  • Cloud-based storage
  • GPUs for computationally intensive and deep learning models

4. Version Controls

Version control predominantly involves managing and tracking changes to the software program. Identifying and resolving errors that may occur after the changes are implemented is crucial.

Code version control is used with DevOps for clear documentation pertaining to the adjustments and changes made to the software. DevOps version control is easy to understand and less complex. It includes only tracking changes made to the code & artifacts.

However, with machine learning, version control is slightly different. Besides code version control, data is another critical factor that needs to be managed along with metadata, logs, and other parameters. In MLOps, version control includes tracking changes made to:

  • The model code
  • Training input data
  • Experiment run

5. Continuous Monitoring

The importance of continuous monitoring can’t be overlooked or denied. Monitoring plays a pivotal role and is a crucial part of DevOps practices. In the last few years, SRE (site reliability engineering) has been the rage emphasizing the importance and benefits of monitoring software development. However, the key difference between MLOps and DevOps is that software programs don’t degrade, but ML models do.

When the model is integrated into system production, it uses new data to generate predictions. The data continues to adapt and change as the business environment. It further causes model degradation. Hence, MLOps includes procedures that promote and facilitate continuous monitoring and retraining. It helps algorithms to be used continuously in production.

6. Team Composition

Another major difference between DevOps and MLOps is team composition. Needless to say, the difference in team composition is much owed to the different nature of end products both methodologies are used for. For example, DevOps teams consist of software and DevOps engineers. The role of software engineers is to develop the software product while DevOps engineers deploy it.

Compared to DevOps, MLOps team composition is quite different. It includes data scientists and machine learning engineers. The role of data scientists is model training. They ensure that the machine learning algorithms are built by examining several examples and then attempting to find the perfect model that aligns with the business objectives seamlessly while minimizing losses. Likewise, the role of machine learning engineers is to monitor and deploy the model successfully into the client’s business landscape.

7. Deployment

Adding features manually and then uploading them is easy with traditional software programs in DevOps. However, deployment is more complex with machine learning. In MLOps, you can’t deploy a machine learning model trained offline. In such a scenario, you will have to create a pipeline for retraining the model and deploying the same automatically. It becomes even more complex as you have to automate the steps for retraining and validating models made manually by the data scientists.

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DevOps or MLOps – Which Should I Opt For?

DevOps or MLOps have some similarities and differences. However, the best part is that businesses can use them independently or in conjunction to improve the company’s ability to work and achieve its targeted goals. For instance, you can use MLOps for automating data analysis while DevOps for the remaining.

Simply stated, there is no one-size-fits-all solution. The use and integration of these methodologies predominantly depend on your specific business goals and requirements. DevOps may be a good fit for a conventional software project, while MLOP may be an excellent choice when the project requires experimentation.

Therefore, it is best to work with a trusted and experienced software development company that can help you evaluate your options and make the best pick. Contact Vates to discuss your project needs and business goals.

Let us help you determine the best methodology for integration and to move forward. At Vates, we also specialize in software testing services. Give us a chance to help you. You will be happy with your decision, like our growing customer base.

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