Model serving is an important step in the ML lifecycle, as it enables organizations to use trained models to make predictions.
Model serving refers to the process of deploying a trained machine learning model to a production environment, where it can be used to make predictions or decisions. There are several steps involved in model serving, including:
Training the model: This involves using a dataset to train the model using a machine learning algorithm. The goal is to optimize the model's performance on a specific task, such as predicting the likelihood of a customer churning or identifying objects in an image.
Validating the model: Once the model has been trained, it is important to validate its performance to ensure that it is accurate and reliable. This can be done through various methods, such as cross-validation or holdout validation.
Saving the model: After the model has been trained and validated, it needs to be saved in a format that can be easily loaded and used in a production environment. This usually involves saving the model's parameters and weights to a file, such as a .h5 or .pb file.
Deploying the model: There are several ways to deploy a machine learning model, including using a cloud-based platform, deploying the model on a physical server, or using a containerization solution such as Docker. It is important to carefully consider the resources and infrastructure required to serve the model in a production environment.
Scaling the model: If the model is expected to receive a high volume of requests, it may be necessary to scale the model in order to handle the increased workload. This can be done through horizontal scaling, which involves adding additional servers to the model serving infrastructure, or vertical scaling, which involves increasing the resources of the existing servers.
Monitoring the model: Once the model is deployed, it is important to monitor its performance and accuracy to ensure that it is functioning as expected. This can be done through various methods, such as logging model predictions and comparing them to actual outcomes, or using monitoring tools to track the model's performance and resource usage.
Overall, model serving is an important step in the machine learning lifecycle, as it enables organizations to use trained models to make predictions or decisions in a production environment. By following best practices and carefully considering the resources and infrastructure required to serve the model, organizations can ensure that their models are accurate, reliable, and able to handle the demands of a production environment.
Model serving explained
This tech talk breaks down what it means to turn your ML models into microservices and API endpoints that can be deployed and run anywhere. We explore the role of containers in model serving and how you can quickly prepare your models for production deployment using an open-source solution, chassis.ml. Follow this link for the Github repo.
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