Displaying 1 to 5 from 5 results

sparklens - Qubole Sparklens tool for performance tuning Apache Spark

  •    Scala

Sparklens is a profiling tool for Spark with built-in Spark Scheduler simulator. Its primary goal is to make it easy to understand the scalability limits of spark applications. It helps in understanding how efficiently is a given spark application using the compute resources provided to it. May be your application will run faster with more executors and may be it wont. Sparklens can answer this question by looking at a single run of your application. It helps you narrow down to few stages (or driver, or skew or lack of tasks) which are limiting your application from scaling out and provides contextual information about what could be going wrong with these stages. Primarily it helps you approach spark application tuning as a well defined method/process instead of something you learn by trial and error, saving both developer and compute time.

icp4d-customer-churn-classifier - Infuse AI into your application

  •    Jupyter

In this code pattern, we will create and deploy a customer churn prediction model using IBM Cloud Private for Data. First, we will load customer demographics and trading activity data into Db2 Warehouse. Next, we'll use a Jupyter notebook to visualize the data, build hypotheses for prediction, and then build, test, and save a prediction model. Finally, we will enable a web service and use the model from an app. The use case describes a stock trader company that can use churn prediction to target offers for at-risk customers. Once deployed, the model can be used for inference from an application using the REST API. A simple app is provided to demonstrate using the model from a Python app.




db2-event-store-iot-analytics - IoT sensor temperature analysis and prediction with IBM Db2 Event Store

  •    Jupyter

This code pattern demonstrates the use of Jupyter notebooks to interact with IBM Db2 Event Store -- from the creation of database objects to advanced analytics and machine learning model development and deployment. The sample data used in this code pattern simulates data collected by real industry IoT sensors. The IoT sample data includes sensor temperature, ambient temperature, power consumption, and timestamp for a group of sensors identified with unique sensor IDs and device IDs. A simple IBM Streams flow is used to stream the sample data from a CSV file to an Event Store table.






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