Model Deployment and MLOps: From Development to Production
Building a Machine Learning model is just the first step—deploying it efficiently into a production environment and maintaining it over time is equally important. This is where MLOps (Machine Learning Operations) comes into play. MLOps is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and management of ML models.
Key Steps in Model Deployment:
Model Packaging – Once an ML model is trained and evaluated, it needs to be packaged into a deployable format. This often involves using frameworks like ONNX (Open Neural Network Exchange), TensorFlow Serving, or TorchServe to ensure compatibility across different environments.
Containerization – Many ML models are deployed using Docker containers, which package the model along with its dependencies, making it portable across different systems.
Deployment Strategies – Models can be deployed using various approaches:
On-premise – Running the model locally or within an organization’s private infrastructure. Cloud-based deployment – Hosting the model on platforms like Google Cloud AI Platform, AWS SageMaker, or Azure Machine Learning.
Edge AI – Deploying models directly on IoT devices, smartphones, or embedded systems for real-time inference.
