MLOps: Everything You Need to Know
Last Updated : 27 May, 2025
MLOps stands for Machine Learning Operations. It is a way of working that helps teams handle everything about a machine learning model from creating and testing it to putting it into use. It brings together data scientists, machine learning engineers and operations teams to make sure the models are built, used, checked and updated smoothly and reliably over time.
MLOps takes inspiration from DevOps and apply similar principles like automation, version control and continuous improvement to machine learning projects.
Why is MLOps Important?
In traditional machine learning workflows several problems can slow things down and reduce effectiveness:
- Lack of Team Collaboration: When teams work separately without talking to each other it causes confusion, delays and mistakes
- Manual Deployment: Manually deploying models takes a lot of time and can cause mistakes
- Poor Version Tracking: It’s difficult to track which version of the model is in use or what changes have been made.
- No Ongoing Monitoring: Once a model is live there’s no system in place to monitor its performance regularly.
- Resource Management Issues: As the project gets bigger it becomes harder to handle computing power and storage without using automation.
MLOps Workflow
A MLOps workflow helps teams to manage machine learning projects smoothly and automatically. Here's how it works:
Mlops Workflow- Collect and Clean the Data: The first step is gathering data from different sources. This data often needs to be cleaned like fixing missing values or removing errors so that the model can learn from it properly.
- Train and Test the Model: Next the cleaned data is used to train the machine learning model. After training the model is tested to see how well it performs.
- Package and Deploy the Model: If the model gives good results it’s packaged (prepared for production) and deployed. This means the model is now ready to make real-world predictions.
- Monitor the Model After Deployment: Once the model is live it’s important to monitor it. Monitoring tools check how well it’s working like checking accuracy, speed or if it’s facing any problems.
- Detect Performance Drops and Restart the Workflow: If the model’s performance goes down. For example, if the incoming data is different from the data it was trained on known as data drift then the system can automatically restart the workflow. This means retraining the model with fresh data.
Benefits of MLOps
Machine Learning Operations’s target is to bridge the gap between collaborating teams which can save time and help everyone align with the business objectives. Let’s discuss the benefits of MLOps:
- Faster Deployment: The process from building the model to using it in real life is done automatically, saving time and effort.
- Better Teamwork: It helps people from different teams like data science, engineering and operations work together more easily.
- Easy Monitoring: Once the model is live, its performance and errors are tracked automatically so there's less manual checking.
- Grows with Your Needs: It’s easy to use this process for small or large projects even when using the cloud or containers.
- More Reliable Results: By keeping track of versions and using automation the process gives stable and repeatable results every time.
MLOps vs DevOps
The key difference between MLOps and DevOps is that MLOps was designed for Machine Learning Models and DevOps was designed to deal with the overall Software Development. Let’s understand the key differences between MLOps and DevOps.
Factors | MLOps | DevOps |
|---|
Scope | MLOps extends the principles of DevOps and deals with the deployment of Machine Learning Models. | DevOps is a collaboration between software and IT to deliver development to the production environment efficiently. |
|---|
Objective | To speed up the deployment of Machine Learning models and continuous improvement in its real-time monitoring. | To speed up the development and delivery process of products/applications with fast and reliable releases. |
|---|
Workflow | Only focuses on deploying machine learning models, creating suitable pipelines, automating the models and real-time monitoring for model performance. | Circumscribes the Software Development Life Cycle from start to end until product/application reaches the production environment and even after it. |
|---|
Tools & Tech | - Model Development : Jupyter Notebook, Keras, Scikit-learn, Fastai, PyTorch.
- Model Deployment Tools: TensorFlow, Docker, Kubernetes, MLfolw, kubeFlow.
- Monitoring Tools: TensorBoard, Kibana, Grafana, Datadog.
| - Version Control Systems: Git, Apache, AWSCodeCommit.
- CI/CD tools: Jenkins, Travis CI, Bamboo, Team City.
- Configuration Management tools: Ansible, Salt, Puppet, CFEngine
- Containerization: AWS, Kubernetes, Azure, Docker
|
|---|
Skill Sets | Fundamentals of Machine Learning, Data Engineering, Model Development, Model Deployment, Containerization, Model Versioning, Monitoring ML models and A/B testing. | Version Controlling. CI/CD, Automation, Containerization, Orchestration, Infrastructure as Code, Monitoring and Logging, Cloud Platforms. |
|---|
Data-Centric Focus | Enhancing the model’s workflow when a large dataset is used is the main focus. | The main concerns of DevOps are the development and maintenance of configurations and infrastructure. |
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