How to integrate AI and Machine Learning into Hybrid apps?

Just like native apps, they are installed from app stores and can access and utilize a device’s core features, such as a Calendar and Camera. However, internally, these apps are much more like web apps, as they are written using common web technologies and programming languages, such as CSS, HTML5, and JavaScript.

A top-rated hybrid app development company can help you build a mobile application that looks and feels like a native app but has a significantly lower development cost.

You can now create more intelligent apps thanks to rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML).

Integrating AI and ML models into a software product requires careful planning and execution.

• Identifying areas that can benefit from the integration: This has to be your first step. However, it’s so simple yet profound that executives often overlook it. There is no point investing in AI and ML if it will add any value to your offering. So, you must deeply analyze why you would like to embed AI and ML into your app's core. What problems are you or your customers facing that can be resolved with a planned integration? Begin with identifying the challenges.
• Plan and budget: To successfully improve your app with new-gen technology, you must build a comprehensive strategy and budget. The strategy must clearly express the desired outcome. A budget, however tentative, will help you shortlist vendors that you should meet and connect with.
• Collect vast amounts of data: To build robust and intelligent AI/ML models, you would need to train them. And for that, you’d need massive amounts of reliable data. Now, you might already have that data. Or, if you do not have it, you might have to collect it.
• Build and integrate customized models: Once the data is available, the next step is to build models and train them rigorously so they can start identifying patterns and behaviors. Once the models or algorithms are fully developed and offer satisfactory results, they can be integrated into the application's backend.
• Testing and validation: This is the fifth phase of model development and integration. In this phase, the testing team will run comprehensive tests on the software to determine whether it’s performing as expected.
• Deployment: After testing, the app, along with the integrated AI/ML models, is released in the public domain or for the intended audience. The app's performance is monitored after the launch, and the team continuously updates its features based on customer feedback.

Conclusion

From Twitter and Gmail to Instagram and Uber, thousands of apps are built using hybrid frameworks and web technologies.

Their unique positioning in the digital space makes them a hot digital asset for brands and businesses seeking increased customer base and engagement. Businesses across industries have been growing in demand for hybrid app development services. With the dramatic rise in the popularity of AI and ML, many forward-looking companies are embedding these intelligent technologies into their apps.

However, integrating new technologies into existing hybrid apps or new apps has its own challenges, such as increased development costs, the need for massive amounts of reliable data, and compliance with data privacy and protection.

Partnering with a reputed hybrid mobile app development services provider can streamline development and integration. The collaboration will help you shift most of the thinking, building, and frustration (involved in the development process) to the app development company.

Mike Kelvin is the business analyst at Kellton.