Speed up AI integration in your apps with embedded AI

Embeddable AI is a set of flexible, enterprise-grade AI capabilities that developers can seamlessly integrate into their applications. This is where containerized libraries come into play. Built on an open-source framework, these libraries offer pre-trained models that significantly reduce the time and resources required for developers to add AI to their applications.

Containerized libraries are not just efficient; they are also versatile. They can run anywhere, are flexible and extensible, and can help reduce infrastructure costs due to their lightweight nature. This makes them an ideal choice for developers looking to enhance their applications with AI capabilities.

How to Add AI to Your Apps Faster with Embedded AI

In this video, Martin Keen from IBM talks about AI deployment, specifically how to deploy embeddable AI, and centers his discussion on the 2 major methods: containerized libraries and applications.

Applications are distinctive types of software precisely designed to execute a certain task or deliver specified functionality to an end user. They are accompanied by a unique array of advantages that can benefit the user in numerous ways. This includes the availability of low or no-code options, which make the application more accessible to those without coding knowledge. With these options, users can interact and engage with the application with ease, without the need to learn coding to leverage the tool.

Additionally, applications also offer a swifter route to get to the market, enhancing the efficiency of business processes. This is especially beneficial for organizations looking to incorporate new technologies and capabilities as they can be delivered rapidly to the users. Furthermore, there is also the advantage of reduced development costs. With an application, certain developmental aspects are handled by the application creating company, reducing the time and resources needed for development in-house.

Incorporating AI into apps

However, incorporating AI into these applications is a significant step and should be handled with utmost consideration. It necessitates being managed in a way that is responsible, trustworthy, and secure. Responsible AI emphasizes providing a governance structure that outlines rules, policies, and assigns accountability throughout the AI lifecycle. This ensures that the AI deployed is ethically sound and complies with all necessary standards and regulations.

Trustworthy AI places significance on data used to train AI. The data must be free from bias and developed with domain-specific expertise, ensuring the AI produced is reliable and provides accurate results. Additionally, a Secure AI safeguards that constant, 24×7 enterprise-grade support is available, providing security and round-the-clock assistance, thereby mitigating any potential disruptions or issues that could impact the operation of the AI.

The decision on whether to use a library or an application to integrate AI largely depends upon various factors such as the solution’s environment where it operates, the compute cost associated with hosting the AI element, and the organization’s go-to-market strategy. Both library and applications possess their unique strengths in terms of flexibility, security, and reliability. Therefore, the chosen approach should align with the specific needs and solution requirements of the company, to ensure optimal usage and effectiveness of the AI implementation.

The success of AI deployment lies in understanding needs and leveraging the strengths of each form factor to drive innovation and unlock the full potential of AI-powered solutions. As the world continues to embrace the AI-plus mindset, the importance of Embeddable AI will only continue to grow.

Source: YouTube

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