Learn more about an API-centric approach to building and maintaining AI-enabled applications
It's estimated that anywhere from 50-90% of AI models developed never make it past the AI "valley of death" that exists between the lab and deployment into production. Overall, API-based approaches to AI integration offer a flexible, scalable, and maintainable way to incorporate AI capabilities into your organization.
API-based approaches to AI integration have several advantages:
Flexibility: API-based integration allows you to use AI functionality in a flexible and modular way, without being tied to a specific implementation or platform.
Reusability: You can reuse API-based AI functionality across multiple applications or systems, making it easier to build and maintain consistent AI capabilities across your organization.
Scalability: API-based integration makes it easier to scale your AI capabilities, as you can add or remove functionality as needed without having to rebuild your entire system.
Ease of integration: API-based approaches make it easier to integrate AI functionality into existing systems, as you can simply call the API and pass it the necessary data, rather than having to build custom integrations for each individual AI model or algorithm.
Maintenance: API-based integration allows you to update or modify your AI functionality without having to rebuild or redeploy your entire system, as you can simply update the API and all of the systems that rely on it will automatically benefit from the changes.
This talk covers how an API-centric approach to building and maintaining AI-enabled applications can bridge the divide between data scientists, software developers, and infrastructure managers and make the power of AI accessible to everyone. Not only is this approach better by making AI more accessible and easier to use, but also offers leaders the sophisticated governance and compliance monitoring needed for AI at scale.
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