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Discover the step-by-step guide on integrating Bubble.io with Azure Machine Learning to simplify and optimize your app development process.
Azure Machine Learning is a cloud-based platform provided by Microsoft for building, training, deploying and managing machine learning models. It offers a suite of services that integrate with existing data tools, allowing users to automate and streamline the machine learning lifecycle. This includes data preparation, model training, model deployment, and model performance tracking. It supports numerous programming languages and is designed for applied machine learning.
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Before proceeding with any kind of integration, you need to setup your workspace on Azure Machine Learning.
Sign in to the Azure portal at https://portal.azure.com/.
On the left-hand menu, click on "+ Create a resource". In the search bar, type "Machine Learning" and select the Machine Learning option from the dropdown list.
Click the "Create" button to start setting up your workspace.
Fill in the details such as your subscription, the resource group (create a new one if you need to), the workspace name, the region, the storage account, and the key vault. Then click "Review + Create".
After the workspace has been validated, click on "Create" to finalize the workspace.
Once the workspace is created, go into it and pin it to your dashboard for easy access.
To use Machine Learning within Bubble, you need a trained model in Azure Machine Learning. Follow these instructions.
In your Machine Learning workspace, click "Authoring" on the left hand side, then "Notebooks".
Click "+ New file", give your file a name and select "Python" as the file type.
Write your Machine Learning code in this notebook to create and train your model.
Go back to your Machine Learning workspace and click "Models" on the left hand side.
Find the model you've just trained, select it, and click "Deploy".
In the deploy form, provide a name for your deployed model and choose "Azure Kubernetes Service" as compute type. Click "Deploy".
Before the Bubble.io and Azure Machine Learning integration, you need to set up a workflow on Bubble.io.
Sign in to your Bubble.io account and select your application.
Go to the Design tab, click "+ Create a New Page", and give this page a name, e.g. "Azure ML Integration".
Add some inputs to the page which corresponds to the input your machine learning model expects.
Add a button to the page to trigger the integration with Azure ML.
Go to the Workflow tab, click "+ Add an action" when this button is clicked.
In the workflow you've created, choose "Plugins" > "API Connector" > "Add another API".
In the new API setup form, give your API a name e.g "Azure ML API" and set authentication to "None or self-handled".
Click "Add another call", give your call a name e.g "Predict", choose "Post" as the call type.
In the URL field, copy and paste the endpoint URL given by Azure Ml after deploying the model. Into the 'Body' paste the sample request JSON that matches the parameters your model expects.
For the Headers, add two keys, "Content-Type" and "Authorization". Set the value of "Content-Type" to "application/json", and for "Authorization", paste the authorization key provided by Azure after deploying your model.
Click "Initialize Call" and wait for Bubble.io to send a request to Azure Ml and set up the connection.
Now, your Bubble.io application is fully integrated with your Azure Machine Learning model! When your users fill the input fields and click the button, Bubble.io will send a HTTP POST request to Azure ML's endpoint, the trained model then returns the prediction, and which can be displayed on your Bubble.io application.
An e-commerce company uses Bubble.io platform for developing their app interface, handling customer product preferences, shopping cart details and creating dynamic user registration forms. The company also uses Azure Machine Learning for predictive analytics to provide better shopping recommendations to customers based on their previous purchases. The company wishes to use these purchase patterns using Azure Machine Learning to optimize the shopping recommendations in real-time on their app built on Bubble.io.
App Development and User Registration Forms:
The company uses Bubble.io for their e-commerce app development, including the creation of user registration forms, product displays and shopping cart management.
User Data Collection:
When a user registers or makes a purchase, the details are captured through the Bubble.io app. This data includes details such as customer's interests, shopping patterns, and specific products added to the cart.
Azure Machine Learning Integration:
The company installs a plugin in Bubble.io that connects with Azure Machine Learning. This plugin is configured with their Azure Machine Learning API key. They also set up workflows in Bubble.io that are triggered by user activities like logging in, making a purchase, or add products to the cart.
Predictive Analytics on User Data:
Using Azure Machine Learning, the company analyses the user data gathered by Bubble.io. Machine learning models predict user's shopping product recommendations based on their previous purchases and other factors like seasonal trends and popularity of products.
Displaying Recommendations:
As customers browse through the e-commerce app, the Bubble.io workflows are triggered, sending user-specific data to Azure Machine Learning. The predictive recommendations generated by Azure Machine Learning are fed back into the app, displaying personalized product recommendations for each user.
Data Insights and Monitoring:
By integrating Bubble.io with Azure Machine Learning, the e-commerce company can track and monitor the effectiveness of their recommendation engine. They can also analyze user shopping behavior and patterns to optimize results.
Personalized Shopping Experience: Using machine learning for predictive analytics allows the e-commerce company to offer a personalized shopping experience for each user. This increases customer satisfaction and boosts sales conversions.
Data-Driven Optimization: The integration allows for constant data-based monitoring of the recommendation system and enables quick iterations based on user behavior, leading to an optimized shopping experience.
Efficient and Actionable Insights: Azure Machine Learning integration allows for the analysis of real-time user activity. This information leads to efficient, actionable insights that enhance the overall business strategy.
Centralized Data Management: All user data is housed and managed within the Bubble.io interface, offering a streamlined process for data management and analysis.
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