Using Bubble and no code to drive the development of machine learning models
Bubble.io enables you to focus on generating models without the need for extensive coding. This flexibility enables rapid experimentation and iterative refinement of your approach.
Building artificial intelligence and machine learning models can unlock many opportunities for your business. To fully capitalise on this potential, it's essential to engage with your data and develop models that extract value from it.
Bubble.io is a development tool that helps you carry out data analysis and model building by integrating your ideas through the tool with an external AI engine. It enables you to iterate model building without the need for extensive coding
The true value is derived from the data models and the application of business domain knowledge to problem-solving. To do this effectively, you need to be as close to the process as possible.
Before diving deeper, it's important to understand what AI and ML are.
Understanding AI and ML
Articial Intelligence (AI): The overarching concept of machines performing tasks that would require intelligence if done by humans.
Machine Learning (ML): A specific subset of AI that involves the development of algorithms allowing machines to learn from and make decisions based on data.
What Can You Do with AI?
In general, much of what businesses need from AI is actually machine learning (ML). Businesses accumulate vast amounts of data that require thorough analysis. While humans can analyse and organise this data, it often requires substantial knowledge and time to ensure accuracy. ML can significantly speed up this process. By passing data to a model, you can train it to generate outcomes and insights about what the data represents.
Learn how Orzo Blue can help you harness AI and Machine learning to develop features to add value to your business.
It’s crucial to validate these outcomes to ensure they are applicable and beneficial to your business. You need to build it in a way to also moderate and validate the outcomes and feed those back into the data model. This process may change over time and ideally you need to be in control to create the strongest model.
The trick is to not only doing this identify outlying data and AI hallucinations but also doing it all at scale.
So how can artificial intelligence and machine learning be used?
Enhanced User Experience: AI can provide personalised experiences for users. By integrating AI-driven features like chatbots, recommendation systems, and predictive analytics, you can tailor your application to meet individual user needs, resulting in higher engagement and satisfaction.
Automation and Efficiency: AI can automate repetitive tasks and processes, reducing manual effort and improving efficiency. This is particularly useful in applications that handle large volumes of data or require frequent user interactions.
Advanced Data Insights: ML can analyse vast amounts of data to uncover patterns and insights that would be difficult to identify manually. This can inform decision-making, optimise operations, and provide valuable business intelligence.
Competitive Advantage: Incorporating AI into your platform can differentiate your product in the market, offering unique features and capabilities that attract and retain users.
It is important to remember, AI is not the feature; it is the solution. In each of these examples, the feature is the outcome of using AI to solve problems for your business or for your users.
Integrating AI with Bubble.io
While Bubble.io lacks native AI capabilities, you can integrate third-party AI services through APIs. Here’s how:
Identify the AI Service: Choose a suitable AI service like Google Cloud AI, IBM Watson, Microsoft Azure AI, or OpenAI.Set Up the API: Sign up for the chosen AI service, obtain API keys, and understand the API documentation.Connect to Bubble.io: Use Bubble.io’s API Connector plugin to integrate the AI service by configuring API calls and mapping inputs and outputs.Design Workflows: Create workflows in Bubble.io that utilize the AI service, such as sending user data for analysis and displaying the results.Test and Optimize: Thoroughly test the integration, and optimize workflows and the user interface for a seamless experience.
So what could these solutions looks like in practical terms? Let’s look at real life business examples that can bring value to your business.
1. Leveraging AI for Marketing and Buying Habits
AI and machine learning can analyse extensive datasets to help businesses understand marketing opportunities and buying habits. Essentially it can produce meaning to data in a way that would be, assuming it was accurate, quicker than human analysis.
For example an auction business, might want to examine auction bidding histories to understand bidder preferences. This includes evaluating what bidders have bid on, items they have added to watchlists, and items they have purchased.
Machine learning can be used to provide personalised recommendations for future auctions, targeting the most likely buyers based on their past behaviour. The data produced through this process can be feed into email marketing and other communications as an effective marketing strategies.
Responses to these campaigns can be feed back into the model for validation, with the aim of creating more accurate future results from the model.
2. Optimising Auction Outcomes
AI can significantly optimise auction outcomes by dynamically adjusting various parameters in real time. Machine learning models can evaluate bidding patterns to determine optimal lot valuations and price dynamics.
During an auction, AI can adjust reserve prices down and adjust bidding increments based on real-time data, to encourage bidding in different ways, and ensuring the best possible auction outcome.
The results of the auction can be compared to the predictions from the data model before the auction and the model can be validated and updated.
3. Enhancing Security and Reliability of Bidders
Machine learning can be instrumental in vetting auction bidders, ensuring security, and enhancing the reliability of participants. Through facial recognition and document verification, AI can be used to confirm bidder identities quickly and accurately.
Behavioural analysis and historical data review help detect fraudulent activities and bidding patterns, while credit checks and comprehensive background checks ensure bidders' financial credibility.
AI can also analyse social media profiles and previous auction history to provide a reputation score for each bidder. Continuous monitoring during the auction process further ensures a trustworthy bidding environment.
These are just some practical examples of how AI can be used. There is so much more that can be done using these tools. The important thing to realise here, is that the value comes from the conjunction of business knowledge and data and the data model you create.
Conclusion
Integrating artificial intelligence and machine learning into your business operations can transform the way you work with data to that generate value.
Tools like Bubble.io simplify this process by enabling you to focus on model development rather than coding.
This is because a no-code approach facilitates rapid experimentation and continuous improvement, ensuring your solutions are always evolving to meet business needs.