The term DevOps has been used for some time to describe the relationship between software developers and IT operations staff. And now a new concept, named MLOps, that combines DevOps and machine learning (ML)has started to make waves for reasons galore.
DevOps and MLOps are both software development strategies which focus on collaboration between developers, operations, and data science. The difference between DevOps and MLOps is that DevOps focuses on application development whereas MLOps focuses on machine learning. There are many more differences between the two, which we will cover later in this programming tutorial.
In this article, we will take a look at what these terms mean, the benefits of MLOps and DevOps, and how they compare and contrast with one another.
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What is DevOps?
DevOps is a culture that encourages and facilitates better collaboration and communication between developers and IT operations personnel. As the name implies, this approach requires developers to work hand-in-hand with their counterparts in operations or infrastructure departments so they can build, test, and deploy software quickly without sacrificing quality or stability.
You can learn more about DevOps by reading our guide: An Introduction DevOps and DevSecOps.
What is MLOps?
MLOps is a set of tried-and-true strategies for automating the machine learning life cycle to bridge the gap between model creation, development, and operations. It combines DevOps with machine learning to avoid “technical debt” in your machine learning projects.
MLOps encompasses a collection of best practices and strategies combining machine learning with DevOps to speed up deploying ML models into production. It attempts, like DevOps, to reduce the time it takes to provide features and upgrades to clients.
You can learn more about MLOps by reading our guide: An Introduction to MLOPs.
Benefits and Downsides of MLOps and DevOps
MLOps helps enterprises achieve long-term value while lowering ML, Data Science, and AI risks. Machine learning may help unearth new revenue streams, save time, and reduce resource costs by optimizing operations, employing data analytics for decision-making, and enhancing customer experience. MLOps automation reduces time-to-market and operating expenses, allowing rapid and strategic decision-making.
DevOps helps in improving the speed of development and deployment. It helps with testing new code so that it can be deployed quickly without having any errors or bugs in the code. This makes it possible for organizations to roll out updates faster than ever before, making them more competitive in their respective industries.
Difference Between DevOps and MLOps
The similarities between MLOps and DevOps are clear. Both have to do with automating processes, using data to make better decisions, using software to make processes more efficient, improving the quality of the product or service provided by an organization, and improving speed of delivery.
DevOps is more about the tools and processes used to automate development and deployment processes, while MLOps is focused on using data insights to make better decisions that impact business outcomes. It should be noted that model training, model testing, and validation are unique to MLOps, but are irrelevant to DevOps.
One key difference between MLOps and DevOps is that MLOps places a greater emphasis on automated machine learning tasks, such as training models. DevOps, on the other hand, focuses more on traditional software development tasks such as code builds and deployments.
While DevOps helps businesses put people first by improving communication between departments, MLOps takes it one step further by putting data first—and using that information to identify patterns across multiple platforms leading to improved customer experiences with less manual intervention from humans (which means better ROI ).
MLOps requires data to build the machine learning model. On the contrary, DevOps data is an output, not an input. In MLOPs, the model must be regularly tested in production for performance degradation due to the accumulation of new data over time. DevOps requires you only to monitor the software application for maintenance.
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Which One Should You Choose: MLOps or DevOps?
MLOps and DevOps are both similar and different. You can use them in conjunction or separately to improve your organization’s abilities to work with machine learning. For example, you might use MLOps to automate a portion of your data analysis and DevOps for the rest. Another option is to combine MLOps tools with tools that support automation in general; this could help streamline your entire workflow.
So, which approach is right for your project? It really depends on your specific needs and goals. If you are working on a machine learning project that requires a lot of experimentation and tuning, then MLOps might be a good fit. If you are working on a more traditional software project, then DevOps might be a better option.
At the end of the day, the best approach for you to adhere to is the one that works best for you and your team. There is no one-size-fits-all solution when it comes to software development, so make sure to choose the approach that makes the most sense for your project.
Trends in DevOps and MLOps
MLOps is a relatively new term gaining traction in the data science and machine learning community. It stands for Machine Learning Operations and refers to managing and deploying machine learning models. Just like with traditional software development, many different stages are involved in MLOps, from model development to testing to deployment.
One of the critical differences between DevOps and MLOps is that the latter often requires more data science and machine learning expertise. This is because working with machine learning models can be more complex than traditional software code. As a result, MLOps teams often need access to specialist tools and knowledge to succeed.
Another difference between DevOps and MLOps is that the focus of MLOps is often on automated model management. This means that MLOps teams will often use tools to automate this process instead of manually deploying models. This can help speed up the model development and deployment process and reduce the chances of errors.
We have a great list of the Best DevOps and DevSecOps Tools to help you choose the right project management tools for your DevOps projects.
Final Thoughts on DevOps and MLOps for Software Development
In this programming methodology tutorial, we examined how MLOps and DevOps methodologies function differently. While they both play an essential role in the success of an organization, they differ significantly in their goals and objectives.
The takeaway here is that there are many ways these two concepts can be implemented in tandem—the only thing that might stop you from using them both effectively is time, knowledge, and resources.
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