How do you serve a machine learning model?

Goals

1. Build a trained model to solve a problem.
2. Deploy the model to their project’s particular serving solution.
3. Use the deployed model to serve users, and obtain feedback such as user interaction data.
4. Deploy the retrained model as a new version.
5. Use monitoring and logging to check the performance of the new version.

How do you serve a model?

How to Serve Models

1. Materialize/Compute predictions offline and serve through a database.
2. Use model within the main application, model serving/deployment can be done with main application deployment.
3. Use model separately in a microservice architecture where you send input and get output.

What is ML model serving?

Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production.

How are machine learning models deployed?

The simplest way to deploy a machine learning model is to create a web service for prediction. In this example, we use the Flask web framework to wrap a simple random forest classifier built with scikit-learn. To create a machine learning web service, you need at least three steps.

How does TensorFlow serving work?

TensorFlow Serving allows us to select which version of a model, or “servable” we want to use when we make inference requests. Each version will be exported to a different sub-directory under the given path.

How do you serve machine learning models with TensorFlow serving and Docker?

Conclusion

1. Install Tensorflow Serving via Docker.
2. Train and save a Tensorflow image classifier.
3. Serve the saved model via REST Endpoint.
4. Make inference with the model via the TF Serving Endpoint.

What is model as a service?

A “Model-as-a-Service” provides the capability to execute simulation models as a service. MaaS solely focuses on the application aspect of a model against data. There are two main usage patterns: (i) The model can be pre-deployed, has a well-known service endpoint, and may be supported by supplemental data services.

What is a model server?

Model servers simplify the task of deploying machine learning at scale, the same way app servers simplify the task of delivering a web app or API to end users. By pointing the model server at one or more trained model files, the model server can now serve inference queries at scale.

How do you deploy reinforcement learning models?

How to deploy Machine Learning/Deep Learning models to the web

1. Step 1: Installations.
2. Step 2: Creating our Deep Learning Model.
3. Step 3: Creating a REST API using FAST API.
4. Step 4: Adding appropriate files helpful to deployment.
5. Step 5: Deploying on Github.
6. Step 6: Deploying on Heroku.

How do you deploy machine learning models with TensorFlow?

For Windows 10, we will use a TensorFlow serving image.

1. Step 1: Install the Docker App.
2. Step 2: Pull the TensorFlow Serving Image. docker pull tensorflow/serving.
3. Step 3: Create and Train the Model.
4. Step 4: Save the Model.
5. Step 5: Serving the model using Tensorflow Serving.
6. Step 6: Make a REST request the model to predict.