The sample notebooks in this repo demonstrate Watson Machine Learning and watsonx.ai capabilities such as:
- Running experiments on model building using AutoAI or Deep Learning
- Deploying third-party models as web services or batch jobs (i.e.: scikit-learn, xgboost, keras, PMMl, SPSS, etc.)
- Monitoring deployments with OpenScale (drift, bias detection)
- Managing model lifecycles (updating the model version, refreshing a deployment)
- Inferencing watsonx.ai foundation models
- Integrating LangChain with watsonx.ai
Notebooks with Python code and the Python SDK can be found in the python_sdk folder. The REST API examples are organized in the rest_api folder.
This section contains sample notebooks with examples of how to serve different types of models, either as online or batch jobs.
| Notebook | Description | cloud | CPD 4.0 | CPD 4.5 | CPD 4.6 | CPD 4.7 | CPD 4.8 |
|---|---|---|---|---|---|---|---|
| Use a custom software spec to create a statsmodels function | Demonstrates how to deploy a Python function with statsmode in Watson Machine Learning. For this, you need to create a custom software specification using a conda yaml file with all of the required libraries. | link | link | link | link | link | link |
| Use a function to recognize hand-written digits | Demonstrates how to create and deploy a function that receives HTML canvas image data from a web app and then sends that data to a model trained to recognize handwritten digits. | link | link | link | link | link | link |
| Use scikit-learn to recognize hand-written digits | Demonstrates how to persist and deploy a locally trained scikit-learn model in Watson Machine Learning. | link | link | link | link | link | link |
| Use scikit-learn and a custom library to predict temperature | Demonstrates how to train a scikit-learn model that uses a custom defined transformer and then how to use it with Watson Machine Learning. | link | link | link | link | link | link |
| Use watsonx, and LangChain to make a series of calls to a language model | Demonstrates how to chain google/flan-ul2 and google/flan-t5-xxl models to generate a sequence of creating a random question on a given topic and an answer to that question. This notebook familiarizes the user with the LangChain framework, using simple chain (LLMChain) and the extended chain (SimpleSequentialChain) with the WatsonxLLM. | link | - | - | - | - | link |
| Use watsonx to tune IBM 'granite-13b-instruct-v2' model with cars4u document | Demonstrates how to do prompt tuning in watsonx. | link | - | - | - | - | link |
| Use watsonx Granite Model Series, Chroma, and LangChain to answer questions (RAG) | Demonstrates how to use Retrieval Augumented Generation (RAG) in watsonx.ai. It introduces commands for data retrieval, knowledge base building and querying, and model testing. | link | - | - | - | - | link |
| Use watsonx to manage Prompt Template assets and create deployment | Demonstrates how to create a Prompt Template Asset and how to create a deployment pointing on it. | link | - | - | - | - | link |
This section contains sample notebooks with examples of how to use AutoAI and Deep Learning experiments. The notebooks show how to trigger such an experiment, work with trained models, and do model comparison, refinery, and finally deployment.
| Notebook | Description | cloud | CPD 4.0 | CPD 4.5 | CPD 4.6 | CPD 4.7 | CPD 4.8 |
|---|---|---|---|---|---|---|---|
| Use AutoAI and Lale to predict credit risk | Demonstrates how to use AutoAI experiments by getting a German credit data set and training the model to predict banking credit. | link | link | link | link | link | link |
| Use AutoAI and timeseries data to predict COVID cases | Demonstrates how to use AutoAI experiments for timeseries data sets in Watson Machine Learning service. | link | - | link | link | link | link |
| Use AutoAI to train fair models | Demonstrates how to use AutoAI experiments with bias detection/mitigation in Watson Machine Learning. | link | - | link | link | link | link |
| Use Lale AIF360 scorers to calculate and mitigate bias for credit risk AutoAI model | Demonstrate how to use AutoAI experiments in Watson Machine Learning. | link | link | link | link | link | link |
| Use PyTorch to recognize hand-written digits | Demonstrates how to use Deep Learning model training and scoring in Watson Machine Learning. | - | link | link | link | link | link |
This section contains sample notebooks with examples that show how to work with the Watson Machine Learning instance.
| Notebook | Description | cloud | CPD 4.0 | CPD 4.5 | CPD 4.6 | CPD 4.7 | CPD 4.8 |
|---|---|---|---|---|---|---|---|
| Machine Learning artifacts export and import | Demonstrates an example of exporting and importing assets using Watson Machine Learning. | link | link | link | link | link | link |
| Machine Learning artifacts management | Demonstrates how to manage and clean up a Watson Machine Learning instance. | link | link | link | link | link | link |
| Space management | Demonstrates how to manage spaces in the context of Watson Machine Learning. | link | link | link | link | link | link |
This section contains sample notebooks with examples that show how to update an existing model version and refresh an existing deployment in-place.
| Notebook | Description | cloud | CPD 4.0 | CPD 4.5 | CPD 4.6 | CPD 4.7 | CPD 4.8 |
|---|---|---|---|---|---|---|---|
| Use python API to automate AutoAI deployment lifecycle | Demonstrates how to use the AI Lifecycle features from the AutoAI model in Watson Machine Learning. | link | - | - | - | link | link |
| Use scikit-learn and AI lifecycle capabilities to predict Boston house prices | Demonstrates how to use the AI Lifecycle features in Watson Machine Learning. | link | link | link | link | link | link |