DevOps and MLOps key differences

MLOps vs DevOps – Key Differences and Use Cases Explained

December 13, 2024
logicrays

MLOps is the abbreviation for Machine Learning Operations. So what is MLOps? For the layman students, it simplyhelps create, deploy and manage machine learning (ML) models in a smooth manner.

If you’re diving into DevOps training or artificial intelligence training, you’re likely to come across these terms. Understanding their key differences and use cases is crucial in today’s tech-driven world.

For example, think about an online store recommending products. The model suggesting these items needs regular updates because user preferences change. MLOps automates this process. From collecting data and training models…to testing and deploying them. MLOps does it all, quickly and reliably.

So what’s the difference between DevOps and MLOps?It’s like DevOps but specifically designed for ML. This ensures ML models work well in real-world systems, even as data or user needs change.

Like in 2024, over 83% of companies using AI have adopted MLOps to improve their software systems. Netflix uses it to recommend shows and Spotify applies it to suggest songs.

The add-on advantage is that MLOps also monitors models after deployment. If predictions go wrong or data patterns change, it alerts engineers to fix issues.

Visual representation of DevOps lifecycle and workflow
Automated server management and deployment in DevOps

DevOps means making software development quick and smooth by making both developers and operations teams work together. Think you are about to be building an online shopping app. Developers create features like a search bar and operations team will ensure it runs well on servers. DevOps means it is their combined effort for software updates to happen quickly without breaking the app.

For example, when Amazon wants to add a “one-click buy” feature, DevOps tools will test it, deploy it and monitor its performance. This automation brings down errors and makes processes faster. Major firms like Netflix use DevOps to roll out updates without interrupting your binge-watch sessions. In 2024, companies using DevOps reported 60% faster software releases and a 35% drop in bugs.

Workflow differences between MLOps and DevOps
Team collaboration in MLOps and DevOps

MLOps vs DevOps – both serve different goals but together enhance modern tech solutions. MLOps is all about improving artificial intelligence and models with continuous data. On the other hand DevOps is focused on keeping the software running smoothly and scaling it when needed. We are now sharing how…:

FeatureMLOpsDevOps
Pipeline ComplexityMore complex because they involve machine learning tasks like procuring data, model training, testing and improving the model over time.   For example, if you are creating a recommendation system for an online shopping website, then you have to collect customer behavior data. Next you train the model to predict what products a customer might like and keep improving by getting more data.Simpler pipelines that focus on automating the testing, building and deployment of software.     In the same shopping website example, DevOps helps by making sure that once a new feature is created (like a “Buy Now” button). Then DevOps ensures it is quickly tested and added to the website without any issues.
Tools and FrameworksMLOps uses frameworks like to train and improve machine learning models.     For example, if it can use TensorFlow to train a recommendation system on data. Cloud computing tools can help deploy this model on the internet and manage its growth.DevOps uses  specific tools for the job.   For the same shopping website, DevOps uses tools like Jenkins to automatically test new features. And also uses Docker to package the software in such a way that the website runs smoothly on any server.
Role of DataData is at the heart of MLOps. You constantly need new customer data for improving the model’s suggestions.   For example, without the latest data your system can’t learn or predict what products a new user/loyal user might like.While data might be used to understand user behavior, DevOps’ job is to ensure that the website or application works without crashes.
Monitoring and MetricsIn MLOps, you monitor the performance of the model and look for signs like data drift or errors in suggestions.   For instance, for a shopping websites, you will have to consistently monitor – if the model is recommending outdated or irrelevant products? If yes, then feed the latest data and make the model upgraded.In DevOps, we monitor the software’s reliability and ensure that everything runs smoothly.   For example, if the shopping website goes down or a feature isn’t working, DevOps will get notifications and fix the issue faced.
Deployment and ScalingMLOps focuses on deploying machine learning models and ensuring they can handle more data as needed.   For example, a recommendation system for millions of users needs continuous updates and scaling to stay accurate.DevOps focuses on deploying applications and ensuring they can handle increased traffic or usage.   For example, during a busy sale, DevOps ensures the shopping website can handle more visitors without crashing.    
MLOps vs DevOps: Workflow challenges
Integration challenges in MLOps and DevOps

MLOps needs fresh data to keep its software models accurate. For instance, a shopping website’s product recommendations will go wrong if it doesn’t update customer behavior data.

DevOps delivers faster updates, while MLOps updates are slower due to model testing.

      MLOps require special equipment like powerful computers or GPUs to train machine learning models. This makes things more complicated. But DevOps can work with regular servers.

        MLOps models can lose accuracy over time and need regular updates, unlike DevOps which doesn’t require constant changes like models do. For example, a weather prediction AI model becomes less reliable if it doesn’t get updated with new data.

        AI and automation shaping MLOps and DevOps
        Evolution of MLOps and DevOps in 2025

        MLOps will stay significant because more businesses need AI and machine learning. MLOps would be important for healthcare, finance management and retail sectors. By 2025, the AI market will be worth $190 billion.

        On another front, it will still be key for software development in areas like tech, e-commerce and finance. Both are needed, but MLOps will grow faster as AI will continue to transform businesses in the next decade.

        CTA:

        In conclusion, while both MLOps and DevOps play crucial roles in the world of tech, they cater to different needs. DevOps focuses on streamlining software development and operations, while MLOps brings a unique focus on machine learning models, their deployment, and continuous monitoring.

        It is estimated that by 2026, over 35% of businesses will employ MLOps practices. The global DevOps will grow at a good pace from $6.6 billion in 2022 to over $20 billion by 2027.

        So, which one is right for you? Well, if you’re working with AI or machine learning projects, MLOps is your go-to. But, if you’re simply looking to automate software delivery and maintain smooth operations, DevOps is the way forward.

        Here at LogicRays Academy, we understand the importance of these practices and can help you master the skills to take your career to the next level. If you wish to know which career is better for you, write to us!

        FAQs:

        What is MLOps?

        MLOps stands for Machine Learning Operations, focusing on automating the process of creating, deploying, and managing ML models. It helps ensure continuous improvements in AI systems.

        How is MLOps different from DevOps?

        While both aim to improve development, MLOps focuses on ML model lifecycle management, whereas DevOps ensures smooth and continuous software deployment and maintenance.

        What tools are commonly used in MLOps?

        MLOps uses tools like TensorFlow for model training and cloud computing services for deployment, unlike DevOps which uses tools like Jenkins and Docker.

        What are the key differences in deployment between MLOps and DevOps?

        MLOps focuses on deploying machine learning models with data updates, while DevOps focuses on software applications and their operational scalability.

        What role does automation play in DevOps?

        DevOps heavily relies on automation to streamline testing, deployment, and monitoring, ensuring faster updates with minimal errors.

        Which industries benefit most from MLOps?

        Industries like healthcare, finance, and retail, which rely on machine learning for decision-making and predictions, benefit significantly from MLOps.

        Is DevOps still important for AI projects?

        Yes, DevOps remains essential for deploying and managing AI applications and ensuring seamless integration into existing software systems.

        Why should businesses focus on MLOps in 2025?

        As AI continues to grow, MLOps is essential for businesses to keep their machine learning models up-to-date and scalable, making it a critical investment for future tech.

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