How do you deploy a machine learning model into production?

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 put a model into production?

Putting Your Models Into Production

1. Source (Unsplash)
2. A simple model with a single hidden layer.
3. Compile the model.
4. Model training output.
5. Save the trained model.
6. Serve the model with docker.
7. Send a request to make new predictions.

How do you deliver a machine learning model?

Tips

1. Find an implementation of a model solving a similar problem.
2. Reproduce the implementation locally in the conditions of the existing model (same dataset and hyperparameters).
3. Slowly tweak the implementation of the model and the data pipeline to match your needs.
4. Rewrite any parts needed.

How do you deploy the machine learning model in the cloud?

An AI Platform Prediction model is a container for the versions of your machine learning model. To deploy a model, you create a model resource in AI Platform Prediction, create a version of that model, then link the model version to the model file stored in Cloud Storage.

How do I save a trained machine learning model in python?

How to save trained model in Python?

1. Step 1 – Import the library. from sklearn import model_selection, datasets from sklearn.tree import DecisionTreeClassifier from sklearn.externals import joblib import pickle.
2. Step 2 – Setting up the Data.
3. Step 3 – Training and Saving the model.
4. Step 4 – Loading the saved model.