Breaking Down MLOps vs DevOps: Similarities and Differences

Machine learning is not just a buzzword anymore. It has been incorporated into important projects. Now in 2023, developers are focusing on how to implement a successful ML project and confidently push it to production.

That’s where MLOps, or machine learning operations, come into the picture, and it is closely tied to DevOps, the “parent” of MLOps.

As DevOps is focused on improving the overall software development process and MLOps is primarily focused on developing and deploying machine learning models, there are multiple commonalities and differences between the two methodologies.

Let’s discuss everything about DevOps vs. MLOps one step at a time.

What Is MLOps?

MLOps is a set of practices and processes for managing the development, deployment, and maintenance of machine learning models in a production environment. 

It aims to bring the principles and practices of DevOps to the field of machine learning, allowing organizations to streamline and automate the deployment of ML models and manage their ongoing performance and maintenance.

There are multiple tools and platforms which provide a range of features for MLOps like experiment tracking, model deployment, monitoring, and maintenance. The choice of tool or platform will depend on the specific needs and requirements of your organization.

Here are a few tools and platforms available for MLOps:

1. TensorFlow Extended (TFX)
2. Kubeflow
3. Apache Airflow
4. MLflow
5. Databricks
6. H20.ai
7. AWS SageMaker

But while implementing MLOps using these dedicated platforms, you might encounter multiple problems.

Challenges of Implementing MLOps

Out of multiple challenges in implementing MLOps, here are a few important ones:

1. ML models may need to be integrated with existing systems, such as databases and data pipelines, which can be a complex and time-consuming process.
2. There may be issues related to model governance, such as accountability, transparency, and ethical considerations, which need to be addressed.
3. ML models may need to be deployed at scale, which can be a challenge due to the complexity of the models and the size of the data.
4. ML models may need to be monitored for performance and maintained over time, which can be a complex and time-consuming process.

To tackle these challenges, you must incorporate specific techniques to extract the most out of an MLOps-driven approach.

Best Practices for MLOps

There are some top best practices you must consider when implementing MLOps in your business:

1. MLOps processes should be automated as much as possible to reduce the risk of human error and improve efficiency.
2. Experiment tracking and version control should be used to keep track of model development, versioning, and performance over time.
3. ML models should be deployed through a CI/CD pipeline to ensure that updates and changes can be quickly and easily deployed to production.
4. ML models should be designed and deployed to scale and be performant to meet the demands of a production environment.

Now that you understand MLOps let’s learn about DevOps.

What Is DevOps?

DevOps is a software development methodology that removes roadblocks and achieves a continuous cycle of iteration and improvement. It aims to improve collaboration and minimize friction in the software development process. 

You can automate and streamline the software development and deployment process using multiple tools and platforms like:

1. Source code management (SCM) tools like Git, Subversion, and Mercurial
2. Continuous integration (CI) tools like Jenkins, Travis CI, and CircleCI
3. Continuous delivery (CD) tools like Ansible, Puppet, and Chef
4. Configuration management tools like SaltStack, Chef, and Puppet
5. Monitoring and logging tools like Nagios, Zabbix, and Logstash
6. Containerization tools like Docker and Kubernetes

But while managing DevOps using these dedicated platforms, you might encounter multiple problems.

Challenges of Implementing DevOps

Here are some of the common challenges you can face when implementing DevOps:

1. DevOps requires a cultural shift within organizations, as it requires close collaboration between development and operations teams. This can be challenging, as traditional silos between these teams may exist.
2. DevOps enables organizations to develop and deploy new features and capabilities rapidly, but this pace of change can be challenging to manage, especially for large and complex applications.
3. Implementing DevOps can be expensive, as organizations need to invest in new tools, processes, and people, and ongoing costs can be high, as systems and processes need to be maintained and updated over time.

Using strategic practices for DevOps, you can address these challenges. Reap the benefits of increased agility, efficiency, and reliability in delivering new features and capabilities to the customers by implementing them.

Best practices for DevOps

There are a few best practices you can incorporate to get the most out of a DevOps-driven software development process:

1. You should focus on delivering software quickly. Also, implement the required strategies to help you roll back the software without wasting time.
2. Business, operations, and development teams must align and collaborate for productive and successful DevOps execution.
3. Stay updated with the latest software technology trends to bypass hurdles in effective technological development.
4. Run your tests on all codes without failing for manual or automated testing. 
5. Integrate automation and use the accurate CI/CD tools for an effective DevOps system.

BrowserStack provides different integrations with the best CI/CD tools as TeamCity, Travis CI, Jenkins, Jira, etc., for better implementation of DevOps. 

It also provides a dedicated cloud Selenium grid of 3000+ real browsers and devices for testing. You can access our in-built debugging tools for identifying and resolving bugs. You can also test using frameworks like  Appium, Espresso, XCUITest, EarlGrey, Cypress, Playwright,  Puppeteer.

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Now that your basics of DevOps and MLOps are clear, let’s dive into understanding the key similarities between the two.

Similarities Between MLOps and DevOps

As MLOps is a subset of DevOps, there are multiple similarities in both ideologies. Have a look:

1. Both MLOps and DevOps require close collaboration between different teams, including development, operations, and data science teams, to ensure smooth and efficient delivery of models and applications.
2. Many MLOps tools and platforms integrate with existing DevOps toolchains, such as Git, Jenkins, and Kubernetes, making it easier for organizations to implement MLOps within their existing DevOps workflows.
3. MLOps and DevOps promote a culture of experimentation, where teams can quickly test and validate new ideas and approaches, reducing the time and cost of delivering new features and capabilities.
4. Both MLOps and DevOps emphasize the importance of monitoring and feedback loops to ensure that models and applications are working as intended and that issues are quickly identified and resolved.

Also, look at a brief comparison to understand the core aspects of DevOps and MLOps.

Differences Between MLOps and DevOps 

DevOps and MLOps are important for your organization to achieve its goals and be successful. Here’s a detailed comparison table to help you understand the differences.

It’s worth noting that MLOps and DevOps are not mutually exclusive, and many organizations will use a combination of both practices to improve their software development processes.

So, let’s understand how to bridge the gap between the two methodologies.

How to Bridge the Gap Between MLOps and DevOps?

Here are some tips for bridging the gap between MLOps and DevOps:

1. Encourage close collaboration between the data science, development, and operations teams, and establish clear lines of communication to ensure that everyone is aligned on goals and processes.
2. Automate as many workflows as possible, including model testing, validation, and deployment, to reduce manual errors and increase efficiency.
3. Continuously evaluate and improve processes and workflows, and encourage experimentation and innovation to ensure that you are using the best possible tools and approaches for MLOps and DevOps.
4. Implement DevOps monitoring and DevOps feedback loops to ensure that models and applications are working as intended, and that any issues are quickly identified and resolved.

But which of the methodologies should you integrate into your business ecosystem?

Which One Should You Choose: MLOps or DevOps?

Choosing between MLOps and DevOps depends on your specific needs and goals. If your organization is focused on developing and deploying machine learning models, then MLOps may be the better choice. 

But if your organization is focused on overall software development, then DevOps may be the better choice. 

MLOps can help you manage the unique challenges of building, maintaining, and deploying machine learning models, like data management, model versioning, and monitoring model performance in production. 

On the other hand, DevOps can help you improve the collaboration and communication between different teams within your organization to increase the overall speed and quality of your software development and deployment.

You can also decide by considering the maturity level of your organization. If it’s in the early stages of ML development, then it’s better to start with DevOps, as it’s a more general practice, and then gradually introduce MLOps as the ML development grows.

So, what’s the future of MLOps and DevOps?

Future of MLOps and DevOps

The future of MLOps and DevOps is likely to be shaped by several trends and developments. 

Cloud platforms will play an increasingly important role in MLOps and DevOps, as organizations look to take advantage of their scalability, reliability, and cost-effectiveness to support the deployment and management of models and applications.

Also, as organizations look to improve the speed and efficiency of model development and deployment, there will be an increased focus on automation, including the use of AI and machine learning algorithms to automate workflows and reduce manual errors.

MLOps and DevOps will continue to evolve and become more sophisticated as organizations look to leverage these approaches to deliver models and applications faster, with higher quality, and with greater reliability.

Both methodologies offer different features and advantages to data science and IT teams.

But ensure that your MLOps and DataOps teams shouldn’t ignore the continuous testing phase of the workflows as it can hinder the desired output.