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Cookbook

Welcome to the UbiOps cookbook!

The UbiOps cookbook is here to provide (new) users with inspiration on how to work with UbiOps. Use it to find inspiration or to discover new ways of working with the UbiOps platform.

With a (free) UbiOps account you can use the cookbook to have example applications running in your own environment in minutes.*

How does it work?

We have three categories in the cookbook, Python recipes, R recipes and ready deployments. Recipes contain full walkthroughs in jupyter notebooks or Rstudio, and ready deployments contain ready-to-go deployment packages which illustrate how to use the deployment package for typical cases.

Ready deployments

steps-overview The ready deployments show how to set up your deployment package for typical use cases. You can download the deployment package, fill in the deployment creation form in the UI, and upload the deployment package. Afterwards you can make a request to the deployment to test it out.

Recipes

steps-overview Every recipe contains a standalone example with all the material you need to run it. They are all centered around a Jupyter Notebook or Rstudio. If you download the recipe folder and run the notebook/script it will build the example in your own UbiOps environment.

The current Python recipes

Topic and link to recipe Functionalities of UbiOps addressed
Creating a training and production pipeline with Scikit Learn in UbiOps Deployments, pipelines
Deploying a TensorFlow model in UbiOps Deployments
Deploying an XGBoost model in UbiOps Deployments
Triggering a deployment/pipeline request from Azure Functions Different forms of requests, integration
Triggering a deployment/pipeline request from Google Cloud Functions Different forms of requests, integration
Using blobs as temporary storage Blobs
Azure Data Factory and UbiOps pipeline interaction tutorial Integration, pipelines
Using Azure ML services to train a model and deploy on UbiOps Integration, deployments
Pipeline that matches, orders and visualises a list of Pokemon Pipelines
Scheduled pipeline that classifies Amazon reviews Request schedules, pipelines
Deploying a recommender model using Apriori in UbiOps Deployments
Integration with MLflow model tuning tool Deployments
Integration with snowflake cloud-based data-warehouse Integration, deployments
Integration with YData for imbalanced datasets Integration, deployments, blobs
Combining R and Python in the same pipeline: the prediction of house prices Deployments, pipelines
Integration with Pachyderm for automated retraining Integration, deployments
Integration with Arthur for data science monitoring Integration, deployments
RFM analysis for Google Sheets with a pipeline Pipelines, requests, environment vars
Comparing ONNX to Tensorflow performance Deployments, requests, performance, metrics

The current R recipes

Topic and link to recipe Functionalities of UbiOps addressed
Combining R and Python in the same pipeline: the prediction of house prices Deployments, pipelines
Deploying an XGboost model Deployments
Deploying an R XGboost pipeline Deployments, pipelines

Requirements

To be able to use the UbiOps cookbook you need three things:

  • You need to have the UbiOps client library installed. For Python this can be done via pip install or via Setuptools. For more information see our GitHub Python page. For R this can be done by installing the devtools package and then using the install_github function. For more information see our GitHub R page

  • If you want to run Python recipes, you need to be able to run Jupyter Notebook. See the installation guide for more information.

  • If you want to run R script recipes, you need to be able to run Rstudio. See the installation guide for more information.

  • You need to have a UbiOps account. You can create a free account here.

*You might need to make some space in your project by deleting deployment versions if you want to run all the examples and stay within the limits of your account.