Real-time Machine Learning (ML) predictions
In this tutorial you will learn how to deploy a real-time data science application into a scalable self-maintained solution. You create a service that predicts bicycle availability in New York, by building the raw data ingestion pipelines, Extract Transform Load (ETL), and predictions.
Quix enables you to harness complex, efficient real-time infrastructure in a quick and simple way. You are going to build an application that uses real-time New York bicycle data and weather data to predict the future availability of bikes in New York.
You will complete all the typical phases of a data science application:
Build pipelines to gather bicycle and weather data.
Store the data efficiently.
Train ML models with historic data.
Deploy the ML models into production and create predictions in real time.
This would traditionally take several people with a wide set of different skills (Data Engineers, Data Scientists and Developers) and weeks of work. However, you will complete this tutorial on your own in a fraction of the time using Quix.
This tutorial has the following prerequisites:
You will need to know how to train an ML model.
Want to learn it?
If you don't already know how to train an ML model, follow our "How to train an ML model" tutorial here.
We take you through the process of getting the code to access the data, running the code in a Jupyter notebook, training the model and uploading your pickle file to Quix.
You will need a Quix account and be logged into Quix.
Go here to sign up if you need a free account.
The parts of the tutorial
This tutorial is divided up into several parts, to make it a more manageable learning experience. The parts are summarized here:
Create a bikes data real-time stream. Access real-time data from New York's CitiBikes API using a ready made Code Sample.
Create a weather forecast data stream. Add weather data using a free weather API.
Visualize the data. View real-time and historic data in the visualization tools.
Get data to train a model. Use the built in tools to get training data.
Deploy pre-trained ML models and produce predictions in real time. Use our pre-trained models to get CitiBike predictions based on historical bicycle availability and weather forecasts. You also use the built-in visualization tools to view the models prediction.
Conclusion. In this concluding part you are presented with a summary of the work you have completed, and also some next steps for more advanced learning about Quix.