Code Patterns

Everything you need to quickly solve real problems is compiled into a Code Pattern, with architecture diagrams, one-click deployment GitHub repositories, and pointers to essential docs.

Perform a machine learning exercise

Dive into machine learning by performing an exercise on IBM Data Science Experience using Apache SystemML.


Build a recommender with Apache Spark and Elasticsearch

This developer pattern demonstrates the key elements of creating a recommender system by using Apache Spark and Elasticsearch.


Robotic calculations and inference agent

This journey takes you through end to end flow of steps in building an interactive interface between NAO Robot, Watson Conversation API & Data Science Experience


Correlate documents

Correlate content across documents using Python NLTK, Watson Natural Language Understanding (NLU) and IBM Data Science Experience (DSX)


Orchestrate data science workflows using Node-RED

Build a web interface using Node-RED to trigger an analytics workflow on IBM Data Science Experience.


Extend Watson text classification

Augment classification of text from Watson Natural Language Understanding with IBM Data Science Experience.


Detect change points in IoT sensor data

Use time series from IoT sensor data, IBM Data Science Experience, and the R statistical computing project to analyze the data and detect change points.


Transform the retail customer experience with APIs on a mainframe

Create retail applications that leverage data from enterprise IT infrastructure using APIs in a hybrid cloud environment -- no mainframe knowledge required.


Apply machine learning to financial risk management

Use machine learning to perform secure, real-time risk assessment and management to help financial institutions more accurately determine credit worthiness.


Use Swift to interpret unstructured data from Hacker News

Learn how to pull data points -- concepts, entities, categories, keywords, sentiment, emotion, etc. -- from Hacker News articles using natural-language service calls from a Swift-based application.


Discover hidden Facebook usage insights

Enrich unstructured data from Facebook using a Jupyter Notebook with Watson Visual Recognition, Natural Language Understanding, and Tone Analyzer, then use PixieDust to explore the results and uncover hidden insights.


Implement voice controls for a serverless home automation hub

Discover how simple it is to build a home automation hub using natural-language services and OpenWhisk serverless technology.


Create a stress-test app for investment portfolios

Make the markets more predictable by building a portfolio stress-testing app using a set of financial web services.


Analyze traffic data from the city of San Francisco

Look at traffic data from the city of San Francisco, create robust data visualizations that allow users to encapsulate business logic, create charts and graphs, and quickly iterate through changes in the notebook.


Create an investment management chatbot

Create a Watson Conversation-based financial chatbot that enables you to query your investments, analyze securities, and use multiple interfaces.