Overview Attendees to the Building Location Aware Applications using Redis workshop will develop a location-aware client-server application that indexes the location of Bike sharing stations in the US and Europe. The primary focus of the workshop will be on using the geospatial indexing functions of Redis to build location aware functionality into mobile and web applications. As part of the workshop, attendees will learn to parse a web feed of bike share data, store and index that data in Redis, map the data using Google Maps, and build a simple API for answering geographic proximity queries. Agenda The workshop take a hands-on approach to learning the material by building a sample application in Python to parse and load a public feed of bike share information into Redis, then build an API to query that data to build a sharing and locating application. The main topics covered in the workshop are: * Introduction to Redis Geospatial Indexing * GBSF Feed Format * Parsing GBSF feed and storing in Redis * Mapping geographic data from Redis using KML file * Querying Redis geographic data * Building an API to access Redis geographic data Time will be allocated to understanding how to build a JSON feed parser as well as some the issues (technical, legal, and being a good citizen) of using a public data feed, as well as how to serve up the queries. Requirements: This workshop is intended primarily for beginner or intermediate programmers interested in learning how to build a location-aware application. Instruction and support materials will be provided in Python. Attendees are welcome to use any language/system of their choice but limited assistance will be available. Attendees should be comfortable with setting up a multiple file development project and familiar with the basics of client-server programming. Attendees should also be familiar with the basics of pulling and running Docker containers. Attendees should bring a laptop with the following software installed: * Text Editor or IDE of their choice * Python 3.6.0 (or later) interpreter * Docker Community Edition (support materials will be provided as a container)
One of the difficulties in deploying a machine learning strategy is ensuring the performance of real-time decisions made using predictive models. The demands for reliability and speed to support real-time predictive systems have increased. Those challenges are made more difficult by the increasing size and complexity of models and algorithms used to improve the accuracy of decisions. There are many different systems that are available to build the learning part of a machine learning pipeline, but may of these systems leave the decision making system as an exercise for the reader. Building customer services to support real-time decision making can be difficult to do reliability and with scale. Instead of building a custom system, we will look at how Redis 4.0 and the Redis-ML module can be used out of the box to provide a real-time decision making service. Starting with a machine learning pipeline implemented using scikit-learn, we will walk through the types of predictive models (decision trees, regressions, etc.) supported by Redis-ML, and the code to load models into Redis, and finally how to implement real-time decision making.