MLOps Lifecycle
MLOps Lifecycle
MLOps (short for Machine Learning Operations) is a set of practices, tools, and principles that combine Machine Learning (ML) and DevOps to streamline and automate the end-to-end lifecycle of machine learning models. It focuses on the development, deployment, monitoring, and governance of machine learning systems in production. MLOps enables teams to work more efficiently and deliver machine learning models that are scalable, reliable, and maintainable.
MLOps Lifecycle Stages
The MLOps Lifecycle is a set of stages and practices designed to manage the development, deployment, monitoring, and governance of machine learning (ML) models in production. It combines the principles of DevOps (for continuous integration and delivery) with machine learning workflows to streamline collaboration between data scientists, software engineers, and operations teams.
Data Collection & Preparation
Gather and clean data to train machine learning models. Data scientists use tools like Pandas, NumPy, or data pipelines to preprocess and organize the data.
Model Training
Data scientists train models using different algorithms, such as decision trees, neural networks, etc. Tools like Jupyter Notebooks, TensorFlow, and PyTorch are commonly used in this phase.
Model Evaluation
After training, the model is evaluated using performance metrics like accuracy, precision, recall, or F1 score. This helps determine how well the model is expected to perform in real-world scenarios.
Model Deployment
After evaluation, the model is deployed for use in production. Deployment might involve integrating the model into a web service using frameworks like Flask, FastAPI, or AWS SageMaker.
Model Monitoring & Retraining
Once deployed, the model is continuously monitored for issues like data drift or performance degradation. If the model’s performance drops, it may need to be retrained with new data.
Automation (CI/CD Pipelines)
Automated testing and deployment are key parts of MLOps. Every time a change is made (e.g., new model, data update), CI/CD tools like Jenkins or GitLab CI trigger tests and deploy new versions of the model.