Machine Learning Python samples and examples

Use samples and examples to help you learn about features and techniques.

Note: To run these samples and examples, you must be using a v2 Watson Machine Learning Service instance, provisioned after September 1, 2020. For details, see Watson Machine Learning Service instance.

Running a sample notebook

To run a sample notebook:

  1. Add a notebook to your project.
  2. Add a notebook from URL.
  3. Replace the authentication placeholders with your credentials, following the guidelines in the Authentication topic.
  4. Run the notebook cells.

Deployment samples

View or run these Jupyter notebooks to see how techniques are implemented using a variety of frameworks. Some of the samples rely on trained models, which are also available for you to download from the public repository.

Sample name Framework Techniques demonstrated
Use Core ML model to predict Boston house prices CoreML Load a sample data set from scikit-learn
Explore data
Prepare data for training and evaluation
Create a scikit-learn pipeline
Train and evaluate a model
Store a model in the Watson Machine Learning repository
Deploy a model as Core ML
Use scikit-learn and custom library to predict temperature Scikit-learn Train a model with custom defined transformer
Persist the custom defined transformer and the model in Watson Machine Learning repository
Deploy the model using Watson Machine Learning Service
Perform predictions using the deployed model
Use Keras to recognize hand-written digits Keras Download an externally trained Keras model with dataset
Persist an external model in Watson Machine Learning repository
Deploy model for online scoring using client library
Score sample records using client library
Use PMML to predict iris species PMML Deploy and score a PMML model
Persist and deploy a Decision Optimization model Decision Optimization Load a DO model file into an Watson Machine learning repository
Prepare data for training and evaluation
Create an DO machine learning job
Persist a DO model Watson Machine Learning repository
Deploy a model for batch scoring using Watson Machine Learning API
Use Python function to recognize hand-written digits Python Use a function to store a sample model then deploy it
Use scikit-learn to recognize hand-written digits Scikit-learn Train sklearn model
Persist trained model in Watson Machine Learning repository
Deploy model for online scoring using client library
Score sample records using client library
Use Spark and batch deployment to predict customer churn Spark Load a CSV file into an Apache Spark DataFrame
Explore data
Prepare data for training and evaluation
Create an Apache Spark machine learning pipeline
Train and evaluate a model
Persist a pipeline and model in Watson Machine Learning repository
Explore and visualize prediction result using the plotly package
Deploy a model for batch scoring using Wastson Machine Learning API
Use Spark and Python to predict Credit Risk Spark Load a CSV file into an Apache® Spark DataFrame
Explore data
Prepare data for training and evaluation
Persist a pipeline and model in Watson Machine Learning repository from tar.gz files
Deploy a model for online scoring using Watson Machine Learning API
Score sample scoring data using the Watson Machine Learning API
Explore and visualize prediction result using the plotly package
Use Spark to predict product line Spark Load a CSV file into an Apache Spark DataFrame
Explore data
Prepare data for training and evaluation
Persist a pipeline and model in Watson Machine Learning repository from tar.gz files
Deploy a model for online scoring using Watson Machine Learning API
Score sample scoring data using the Watson Machine Learning API
Explore and visualize prediction result using the plotly package
Use SPSS to predict customer churn SPSS Working with the instance
Online deployment of SPSS model
Scoring data using deployed model
Use Tensorflow to recognize hand-written digits Tensorflow Download an externally trained Tensorflow model with dataset
Persist an external model in Watson Machine Learning repository
Deploy model for online scoring using client library
Score sample records using client library
Use XGBoost to classify tumors XGBoost Load a CSV file into numpy array
Explore data
Prepare data for training and evaluation
Create an XGBoost machine learning model
Train and evaluate a model
Use cross-validation to optimize model’s hyperparameters
Persist a model in Watson Machine Learning repository
Deploy a model for online scoring
Score sample data

Deep Learning samples

View or run these Jupyter notebooks to see how deep learning model techniques are implemented using a variety of frameworks. Some of the samples rely on trained models, which are also available for you to download from the public repository.

Sample name Framework Techniques demonstrated
Use Keras and HPO to recognize hand-written digits Keras Working with the Watson Machine Learning service.
Training Deep Learning models (TensorFlow)
Saving trained models in Watson Machine Learning repository
Online deployment and scoring of the trained model
Use Keras to transfer image style Keras Working with Watson Machine Learning service
Training Deep Learning models (Keras)
Use PyTorch to recognize hand-written digits PyTorch Working with Watson Machine Learning service
Training Deep Learning models (TensorFlow)
Saving trained models in Watson Machine Learning repository
Online deployment and scoring of trained model
Use TensorFlow to recognize hand-written digits TensorFlow Working with Watson Machine Learning service
Training Deep Learning models (TensorFlow)
Saving trained models in Watson Machine Learning repository
Online deployment and scoring of trained model
Use distributed TensorFlow to predict digits TensorFlow Working with Watson Machine Learning service
Training Deep Learning models (TensorFlow)
Saving trained models in Watson Machine Learning repository
Online deployment and scoring of trained model

AutoAI samples

View or run these Jupyter notebooks to see how AutoAI model techniques are implemented.

Sample name Framework Techniques demonstrated
Use AutoAI and Lale to predict credit risk Hybrid (AutoAI) with Lale Work with Watson Machine Learning experiments to train AutoAI models
Compare trained models quality and select the best one for further refinement
Refine the best model and test new variations
Online deployment and score the trained model