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Displaying 1 to 20 from 142 results

BluePic - BluePic is a sample photo sharing application for iOS that shows you how to connect your mobile application with Kitura, Bluemix services, and OpenWhisk actions

  •    Swift

Read this in other languages: 한국어, Português,中国. BluePic is a photo and image sharing sample application that allows you to take photos and share them with other BluePic users. This sample application demonstrates how to leverage, in a mobile iOS 10 application, a Kitura-based server application written in Swift.

ibm-cloud-functions-serverless-apis - Create a serverless, event-driven application with Apache OpenWhisk on IBM Cloud Functions that executes code in response to HTTP REST API calls

  •    Javascript

Read this in other languages: 한국어. This project shows how serverless, event-driven architectures can execute code that scales automatically in response to demand from HTTP REST API calls. No resources are consumed until the API endpoints are called. When they are called, resources are provisioned to exactly match the current load needed by each HTTP method independently.

ibm-cloud-functions-serverless-iot-openfridge - Improving customer service with IoT diagnostics and serverless, event-driven analytics

  •    Javascript

Read this in other languages: 中国, português. This project demonstrates serverless technology - powered by Apache OpenWhisk on IBM Cloud Functions - in a smarter home scenario where appliances send diagnostic readings to the cloud for analysis and proactive maintenance.

ibm-cloud-functions-serverless-ocr-openchecks - Serverless bank check deposit processing with object storage and optical character recognition using Apache OpenWhisk powered by IBM Cloud Functions

  •    Javascript

This project demonstrates serverless technology - powered by Apache OpenWhisk with IBM Cloud Functions - in the context of a retail banking scenario where deposited checks are processed digitally (such as through a mobile banking app) using optical character recognition (OCR). This sort of use case is ideal for a serverless architecture because it addresses compute-intensive and highly elastic payday deposit processing where the workload spikes for one particular timeframe every two weeks.




Integrate-Investment-Portfolio - Integrate Investment Portfolio service with your brokerage account

  •    Jupyter

In this developer journey, we will integrate a user's brokerage portfolio (e.g. e*Trade, charles schwab, Fidelity) with IBM's Investment Portfolio service. The integration will use Quovo's Aggregation API to aggregate user's portfolio account and post it to the Investment Portfolio service. The steps to perform the integration will be done using Jupyter Notebook with Python scripts. The IBM Data Science Experience provides a great place to work with notebooks, in addition to other data analytical tools and services. In this journey, we will use IBM Data Science Experience for walking through steps in our notebook. In addition, the steps have been put together to create a web application that performs the integration of user's brokerage portfolio data with Investment Portfolio service. Follow these steps to setup and run this developer journey. The steps are described in detail below.

janusgraph-utils - Develop a graph database app using JanusGraph

  •    Java

Tools for JanusGraph database

Java-MicroProfile-on-Kubernetes - This application demonstrates the deployment of a Java based microservices application using Microprofile on Kubernetes Cluster

  •    Shell

Read this in other languages: 한국어、中国. This code demonstrates the deployment of a Java based microservices application using MicroProfile and Microservice Builder on Kubernetes.

kafka-streaming-click-analysis - Use Kafka and Apache Spark streaming to perform click stream analytics

  •    Jupyter

Clickstream analysis is the process of collecting, analyzing, and reporting about which web pages a user visits, and can offer useful information about the usage characteristics of a website. Recommendation generation on shopping portals: Click patterns of users of a shopping portal website, indicate how a user was influenced into buying something. This information can be used as a recommendation generation for future such patterns of clicks.


Kubernetes-container-service-GitLab-sample - This code shows how a common multi-component GitLab can be deployed on Kubernetes cluster

  •    Shell

Read this in other languages: 한국어、中国 . This project shows how a common multi-component workload, in this case GitLab, can be deployed on Kubernetes Cluster. GitLab is famous for its Git-based and code-tracking tool. GitLab represents a typical multi-tier app and each component will have their own container(s). The microservice containers will be for the web tier, the state/job database with Redis and PostgreSQL as the database.

loopback-in-five - Set up a REST API in 5 minutes with Loopback

  •    Javascript

Express.js made it easy to roll up a REST API in Node.js, and it's become the de-facto library for standing up a backend. Strongloop took that and made it even easier to create your APIs. Strongloop created LoopBack, a tool that allows you to generate a Node.js API that's architected like Express.js, and adheres to the OpenAPI spec. You can have a production-ready web API online with Cloud Foundry and ready to go within 10 quick minutes, and it's flexible and scalable to any needs you might have as it grows.

manage-control-device-node-red - Create, connect and simulate devices with Watson Data Platform and Node-Red

  •    Javascript

Build an IoT project with a simualted device that sends events for data monitoring on Watson IoT Platform on IBM Cloud. This guide steps you through the process of connecting devices to Watson IoT Platform, monitoring and acting on device data.

microservices-traffic-management-using-istio - Istio is an open platform that provides a uniform way to connect, manage, and secure microservices

  •    Java

Read this in other languages: 한국어. Microservices and containers changed application design and deployment patterns, but along with them brought challenges like service discovery, routing, failure handling, and visibility to microservices. "Service mesh" architecture was born to handle these features. Applications are getting decoupled internally as microservices, and the responsibility of maintaining coupling between these microservices is passed to the service mesh.

node-red-dsx-workflow - This journey helps to build a complete end-to-end analytics solution using IBM Data Science Experience

  •    Jupyter

IBM Data Science Experience can be used to analyze data using Jupyter notebooks. There is no mechanism exposed by Data Science Experience to trigger execution of the notebook cells from outside. If this capability is added, we can build a complete end to end analytics solution using IBM Data Science Experience. The below two requirements are addressed by this journey to help build a complete analytics solution with IBM DSX.

personal-wealth-portfolio-mgt-bot - Code for a Cognitive IBM Pattern that uses the Financial APIs (Investment Portfolio & Simulated Instrumented Analytics) on IBM Cloud

  •    CSS

Read this in other languages: 한국어, 中国, Português . Click here to view the IBM Pattern for this project.

pixiedust-facebook-analysis - A Jupyter notebook that uses the Watson Visual Recognition, Natural Language Understanding and Tone Analyzer services to enrich Facebook Analytics and uses PixieDust to explore and visualize the results

  •    HTML

In this Code Pattern, we will use a Jupyter notebook to glean insights from a vast body of unstructured data. Credit goes to Anna Quincy and Tyler Andersen for providing the initial notebook design. We'll start with data exported from Facebook Analytics. We'll enrich the data with Watson’s Natural Language Understanding (NLU), Tone Analyzer and Visual Recognition.

pixiedust-traffic-analysis - A Jupyter notebook using PixieDust and PixieApps to visualize San Francisco traffic accidents

  •    HTML

In this Code Pattern we will use PixieDust running on IBM Data Science Experience (DSX) to analyze traffic data from the City of San Francisco. DSX is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data. The intended audience for this Code Pattern is application developers and other stakeholders who wish to utilize the power of Data Science quickly and effectively.

powerai-market-sentiment - Built for developers familiar with IBM Power systems that are looking to leverage IBM's new PowerAI offering for machine learning

  •    Jupyter

Read this in other languages: 한국어. In this Code Pattern we will use a Jupyter notebook to showcase an example of machine learning with time series on IBM Power8 systems. The notebook will focus on evalulating the predictability of future financial market values in the "renewable energy" sector by examining related markets and sentiment detected in New York Times news articles.

powerai-transfer-learning - Image recognition training with TensorFlow Inception and transfer learning

  •    Python

Read this in other languages: 한국어. The pre-trained Inception-v3 model achieves state-of-the-art accuracy for recognizing general objects with 1000 classes. The model extracts general features from input images in the first part and classifies them based on those features in the second part. We will use this pre-trained model and re-train it it to classify houses with or without swimming pools.

powerai-vision-object-detection - Use deep learning to create a model and a REST endpoint to allow your app to detect, locate and count your product on store shelves

  •    Javascript

In this Code Pattern, we will use PowerAI Vision Object Detection to detect and label objects, within an image, based on customized training. This example can easily be customized with your own datasets.

Predictive-Market-Stress-Testing - Code for a Developer Journey that uses the Financial APIs (Investment Portfolio, Predictive Market Scenario and Simulated Instrumented Analytics) on Bluemix

  •    Python

In this developer journey, we will use three Bluemix finance services to create a web application which performs stress test on an investment portfolio. The Investment Portfolio service is used to load the portfolio into the interface. The Predictive Market Scenario service will create a scenario csv file using risk factor and shock magnitude from user inputs. The Simulated Instrument Analytics service uses the scenario csv file with each holding in the portfolio to create a table displaying the current and stressed price of the investment holding. Be sure to load investment portfolio before running the application.

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