What is MLOps?
MLOps stands for Machine Learning Operations. It is a set of methods that help data science and engineering teams manage the entire machine learning process from collecting data and training models to deploying and monitoring them in real-world systems. You can think of MLOps as the bridge between machine learning and daily operations. It brings structure, order and automation to machine learning projects just like how DevOps does for software development.
Why is MLOps Important?
Imagine you build a model to predict house prices and it works well on your computer. But when you try to use it on a website problems arise like choosing the right model version, handling updated data, retraining regularly and checking performance. This is where MLOps helps. It ensures:
- All your work is well organized and versioned
- You can easily repeat model training and testing
- The model can be updated and used in real-world applications without extra effort
In short MLOps brings consistency, reliability and efficiency to machine learning workflows.
Stages of a Machine Learning Project
Before we understand how MLOps works let's look at the basic steps in a typical machine learning project:
- Data Collection and Storage: Collect data and save it safely
- Data Processing: Clean and prepare the data
- Model Training: Use data to train a model
- Model Testing: Check if the model gives correct results
- Model Deployment: Put the model into use e.g., on a website or app.
- Monitoring and Maintenance: Watch the model’s performance and update it as needed
How MLOps Works
MLOps focus on automating and improving the above steps using a combination of tools and practices. Let’s go through each step and see how MLOps work:
1. Data Storage and Version Control
Machine learning models depend heavily on the quality of the data they are trained with. So it’s important to store data properly and keep track of different versions. The tools used are:
- MinIO or Amazon S3 to store large files or datasets
- DVC (Data Version Control) to keep track of which version of the data is used
How it works:
- When you store your data using DVC it creates a
.dvcfile that contains a unique ID called as hash. - This helps you return to any version of the data anytime.
2. Creating Pipelines with Kubeflow
Instead of running each step like training or testing manually you can create a pipeline which is a series of steps that run automatically in the correct order. The tool we used to create pipelines is Kubeflow Pipelines
In Kubeflow:
- You design steps like data loading, training, testing and evaluation.
- These steps are connected and run one after the other.
- You can see the entire pipeline visually in a dashboard.
- You can change settings like model type or learning rate and compare results.
3. Automation with GitHub and GitOps
Once your pipeline is ready automate it to run whenever data, code or model settings change. This keeps your workflow up-to-date without manual effort. This is possible using:
- GitHub: To store your code and configuration
- GitHub Actions: To trigger the pipeline automatically when changes occur
- GitOps: To connect Git with deployment systems for smooth automation
Difference between MLOps and DevOps
MLOps | DevOps |
|---|---|
MLOps is used for machine learning projects. It includes data preparation, model training, testing, deployment and monitoring. | DevOps mainly focused on Development, testing and deployment. |
MLOps handles the versioning of data and models. | DevOps didn't focus on versioning. |
In MLOps, the primary artifacts are machine learning models, data pipelines and feature engineering processes. | In DevOps, the primary things are source code, application binaries, configuration files and infrastructure as code. |
MLOps emphasizes model performance, data drift and concept drift for monitoring. Involves specific ML metrics. | In DevOps, it will monitor application performance, system metrics and user experience. Uses traditional IT metrics. |
Tools and Technologies are specific ML tools like TensorFlow, PyTorch, scikit-learn and model serving frameworks. | Tools and Technologies are CI/CD tools like Jenkins, GitLab CI/CD and container orchestration tools like Kubernetes. |
Teams required: Cross-functional teams may include data scientists, ML engineers, data engineers and DevOps. | Teams required: Developers, IT operations, quality assurance and other stakeholders. |
Benefits of MLOps
MLOps helps make machine learning projects smoother and more reliable in many ways:
- Faster Model Training and Deployment: Automating tasks speeds up model training and put into use.
- Easier Experimentation: It becomes simple to try out different models and data to find what works best.
- Less Human Error: Automation reduces mistakes that can happen when tasks are done by hand.
- Repeatable and Trackable Steps: Every part of the process can be repeated and checked making it easier to understand and fix problems.
- Better Teamwork: Clear processes and shared tools help teams work together more smoothly.
Limitations of MLOps
- Complex Setup: Setting up MLOps can be difficult and take a lot of time especially for beginners or small teams without much technical experience.
- High Costs: The tools and infrastructure needed for MLOps can be expensive making it harder for smaller projects to afford.
- Limited Flexibility: Some MLOps systems are not very flexible which can make it challenging to try new ideas or quickly change methods.
- Data Privacy and Security: Handling sensitive data in MLOps workflows requires extra care to protect privacy and keep information safe.
- Integration Challenges: Connecting MLOps with existing tools and processes can be complicated and may slow down early stages of development.
