Watson Machine Learning Python client samples and examples
Review and use sample Jupyter notebooks that use the Watson Machine Learning Python library to demonstrate machine learning features and techniques. Each notebook lists learning goals so you can find the one that best meets your goals.
The samples are built by using the V4 version of the Watson Machine Learning Python client library.
Watch this video to learn how to train, deploy, and test a machine learning model in a Jupyter notebook. This video mirrors the Use scikit-learn to recognize hand-written digits found in the Deployment samples table.
This video provides a visual method as an alternative to following the written steps in this documentation.
Watch this video to learn how to test a model that was created with AutoAI by using the Watson Machine Learning APIs in Jupyter notebook.
This video provides a visual method as an alternative to following the written steps in this documentation.
Helpful variables
The pre-defined PROJECT_ID environment variable makes it easier to call the Watson Machine Learning Python client APIs. PROJECT_ID is the guid of the project where your environment is running.
Deployment samples
View or run these Jupyter notebooks to see how techniques are implemented using various 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 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 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 Watson 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 | Work with the instance Perform an online deployment of the SPSS model Score data using deployed model |
| 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 |
| Predict business for cars | Spark | Set up an AI definition Prepare the data Create a model Deploy and score the model Define, store and deploy a Python function |
| Deploy Python function for software specification | Core | Create a Python function Create a web service Score the model |
| Machine Learning artifact management | Core | Export and import artifacts Load, deploy and score externally created models |
| Use Decision Optimization to plan your diet | Core | Create a diet planning model by using Decision Optimization |
| Use SPSS and batch deployment with DB2 to predict customer churn | SPSS | 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 scikit-learn and AI lifecycle capabilities to predict Boston house prices | Scikit-learn | 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 with AutoAI lifecycle capabilities |
| German credit risk prediction with Scikit-learn for model monitoring | Scikit-learn | Train, create, and deploy a credit risk prediction model with monitoring |
| Monitor German credit risk model | Scikit-learn | Train, create, and deploy a credit risk prediction model with IBM Watson OpenScale capabilities |
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 Deploy and score the trained model |
| Use AutoAI to predict credit risk | Hybrid (AutoAI) | 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 Deploy and score the trained model |
Additional resources
From the Gallery, you can review or run a series of end-to-end industry accelerators that demonstrate a range of services and solutions. For details on accessing and running these samples, refer to Industry accelerators.
Parent topic: Training and deploying machine learning models in notebooks