How to Use Low-Code to Enable Machine Learning

Can low-code enable machine learning? Certainly. In fact, it’s an excellent method for simplifying and accelerating the use of machine learning–and enabling developers without data science backgrounds to build software powered by machine learning. In this blog, I make the case for using low-code to develop machine learning apps.

Understanding Low-Code and Machine Learning

ML teaches computers to learn from data all on their own, without your having to hold their digital hands. It is used in all kinds of applications, from detecting spam in emails to making product recommendations in ecommerce. Yet, integrating machine learning into applications can be challenging, especially for developers that don’t have a background in these areas.

The low-code development we all know and love is a streamlined approach that puts you on the fast-track to making software. And if you are building mission-critical or consumer applications, high-performance low-code puts you on an even faster track. It’s all about cutting down on manual coding, so you don't have to work your fingers to the bone. Low-code platforms–particularly high-performance low-code platforms–are therefore well-suited to helping developers avoid the complexity of integrating machine learning into applications. This is commonly known as artificial intelligence (AI) low-code, machine-learning low-code, or machine-learning app development.

It’s a valid alternative to script-based AI development because it eliminates the barrier of coding, and it reduces the artificial intelligence learning curve.

The result? Organizations can create intelligent applications–from chatbots to predictive analytics–without ramping up teams on extensive AI expertise.

Machine learning frameworks and libraries are used to train models on large datasets. Once a satisfactory machine learning model is developed–which takes time and effort–it's integrated into a software application. But, deploying machine learning models into applications can be tricky. It often requires a hand-off from a data science team to machine learning engineers or a development team so they can incorporate the model into the app's backend.

Low-code AI reduces the complexity and effort of this process by providing connectors and components that reduce the coding and scripting work so developers and data scientists can experiment and easily deploy what works into applications.

How Low-Code and Machine Learning Can Accelerate Innovation

For most organizations, the challenges of application development are well-known. Building enterprise applications is hard, there are too few developers, and the business environment changes faster than the software and systems can handle. By enabling machine learning with low-code AI, your company can change the way it delivers enterprise applications. Your teams can truly focus on innovation and business value, knowing that the low-code platform can simplify and expedite the more complicated aspects of developing AI-powered applications. Here's how:

• Faster development: Low-code platforms enable developers to add machine learning to applications in a fraction of the time compared to the traditional method of integration. This speed is essential for businesses looking to quickly release innovative AI-powered solutions to market.
• Democratized development: Low-code platforms democratize app development because they don’t require people to have years of coding experience or days and months of traditional development to participate. This means that subject matter experts can contribute to building AI-driven apps, reducing the burden on specialized AI developers.
• Rapid prototyping: Low-code machine learning enables rapid prototyping of AI applications. This means that data, development, and IT teams can quickly test and iterate on their ideas and see if they line up with business needs and expectations.
• Seamless integration: Adding machine learning to applications to drive innovation is easier with low-code platforms. Many offer pre-built connectors and integrations so that machine learning models almost flow into existing systems and new software.

Enabling Machine Learning in App Development with OutSystems

Adding machine-learning to apps using the OutSystems high-performance low-code platform is simple and fast. It provides a seamless path to using AI for automation and building applications infused with artificial intelligence. Applications to predict churn, detect fraud, personalize user experiences, or use predictive modeling can be created in a fraction of the time it usually takes.

How Does OutSystems Support Machine Learning App Development?

A visual development environment, numerous integration possibilities, and pre-built components and connectors from the Forge enable you to use OutSystems to turn an application into an ML or AI powerhouse by just dragging and dropping. Plus, the platform simplifies the process of modifying existing applications with new AI capabilities. The next thing you know, you’ve added ChatGPT to an insurance application. Or, you’ve built an AI agent that offers personalized experiences. Or you’ve got a better sense of how your customers or employees are feeling because you’ve deployed natural language processing (NLP) and sentiment analysis across your apps and systems.

Access to a wide range of connectors to popular generative AI (like OpenAI) and cognitive services from Microsoft, Google, AWS, and IBM in the OutSystems Forge help speed up app delivery time. And to support your low-code AI and machine learning application development, here are a couple of examples.

OutSystems Azure Open AI Connector

This connector helps you take your artificial intelligence endeavors to new heights by effortlessly connecting you to Microsoft Azure OpenAI and prioritizing your security and compliance. With Azure OpenAI, your data remains under your control, because OutSystems never uses customer data to retrain models. With the Azure OpenAI Connector, you can integrate three essential operations into your applications:

• Completions: Imagine having an AI assistant that gives you accurate predictions based on a simple prompt. This operation generates single or multiple completions tailored to your needs–from streamlining workflows to sparking creative inspiration.
• Chat completions: Create compelling completions for chat messages using the ChatGPT and GPT-4 models. This functionality elevates customer interactions and enhances user experiences in your applications.
• Embeddings: Get vector representations of any input, so that integrating with machine learning models and other algorithms is effortless. With this operation, you can improve data analysis, enhance recommendation systems, and accelerate R&D.

ChatGPT Connector

This connector makes it easier to add ChatGPT to your applications. The range of possibilities this connector opens is exciting. Here’s a real-life use case: comparing insurance policies. When you embed the ChatGPT connector in an application, policyholders can instantly evaluate different insurance policies. They can upload their insurance policy documents, and ChatGPT will analyze the similarities and differences between the policies, generating a summary that highlights key variations. Based on this analysis, the policyholder can decide whether to renew the current policy or choose a different plan or insurer.

As you can see, OutSystems connectors are designed to make it easier to enable machine learning. But that’s not all. The platform also takes care of the non-functional requirements you need to support your ML marvels and provides you with some special AI-assisted development sauce as follows

Scalability

The OutSystems platform provides the vertical and horizontal scalability needed for the most complex consumer experiences, including handling millions of simultaneous users when needed without affecting speed and performance. It is perfectly suited to supporting applications that require machine learning at scale, such as large-scale recommendation engines or predictive analytics.

Security

Security is paramount in AI and machine learning applications, which is why over 500 validations from design to runtime ensure that the machine learning applications you build with the OutSystems low-code platform are secure. Fixes for DDOS, newly identified code vulnerabilities, mobile threats, and other protections are automatically applied to your apps.

Availability

Optimal performance and zero downtime are mandatory requirements for critical systems. With OutSystems, the machine-learning apps you build are always available even during and after a disaster. The OutSystems low-code platform is certified to be compliant with ISO 22301, and the OutSystems cloud automates much of the work associated with business continuity.

AI-Assisted Development

OutSystems not only enables developers to build applications infused with AI and machine learning, but it also provides AI that can guide them as they go. The OutSystems AI Mentor System is a set of ground-breaking AI-based development and quality analysis tools that support your teams throughout the software development lifecycle. No matter where your developers are in the process of building and deploying applications, they get expert-level guidance from AI right in the platform.

Explore All the Ways OutSystems Low-Code Enables ML

Low-code is revolutionizing the way businesses innovate and use AI and ML capabilities. By combining the speed and simplicity of low-code development with the intelligence of machine learning, you can create cutting-edge applications that meet customer expectations and stay ahead of the competition. I encourage you to explore the OutSystems platform and see how it enables machine-learning app development with OutSystems AI.