Watson Machine Learning Python client samples and examples
Review and use sample notebooks that use the Watson Machine Learning Python library to demonstrate machine learning features and techniques.
The samples are built using the V4 version of the Watson Machine Learning Python client library.
Using the Watson Machine Learning Python client in a notebook:
- Click Add to project, and then choose Notebook.
- Specify a name for the notebook.
- Accept the default language, Python, and accept the default runtime.
- Click Create.
- Enter your credentials and import the Watson Machine Learning APIs
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 following 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 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.
Using variables in a notebook
There are several pre-defined environment variables that make it easier to call the Watson Machine Learning Python client APIs.
- USER_ACCESS_TOKEN: The access token that can be used for authenticating the current user in WML API calls.
- PROJECT_ID: The guid of the Watson Studio project where your environment is running
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 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 | Work with the instance Perform an online deployment of the SPSS model Score 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 |
| Predict business for cars | Hybrid(Tensorflow) | Set up an AI definition Prepare the data Create a Keras model using Tensorflow 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 R Shiny app to create SIR model | R | Persist a R Shiny app in in Watson Machine Learning asset repository. Deploy application for online scoring using client library. Score sample records using client library. |
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 PyTorch to recognize hand-written digits | PyTorch | Working with Watson Machine Learning service Training Deep Learning models (TensorFlow) Save trained models in Watson Machine Learning repository Perform online deployment and score the trained model |
| Use TensorFlow to recognize hand-written digits | TensorFlow | Work with Watson Machine Learning service Train Deep Learning models (TensorFlow) Saving trained models in Watson Machine Learning repository Perform online deployment and score the 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 Perform online deployment 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 Perform online deployment and score the trained model |
| Use AutoAI and multiple data to predict sale quantity | Hybrid (AutoAI) | Work with Watson Machine Learning experiments to train AutoAI models using multiple data sources Define Watson Machine Learning experiment for multiple data sets Work with experiments to train AutoAI models Compare trained models quality and select the best one for further deployment Perform batch deployment and score the trained model |
Parent topic: Training and deploying machine learning models in notebooks