Deployment of Generative AI in your existing IT ecosystem
You have all started working on how Generative AI can be incorporated into the existing IT ecosystem so that you can start getting the benefits. It is a critical and vital area to address, as every organization uses and has quite a different applications and systems portfolio. It leads to a challenge of how Generative AI will fit into the overall enterprise architecture.
This week, I have learned five significant approaches for deploying #GenerativeAI in various applications, according to Gartner.
1 – Consume: Gen AI Embedded in Applications
In this approach, generative AI is already integrated within applications. Users consume the AI capabilities as they are without needing to modify or customize the underlying AI models.
2 – Embed: Gen AI APIs in Custom Application Frameworks
Here, generative AI capabilities are accessed through APIs and embedded into custom applications. It allows for a more tailored use of AI functionalities within a specific application framework.
3 – Extend: Gen AI Models via Data Retrieval
This method extends existing generative AI models by retrieving and incorporating external data. This approach enhances the AI model’s capabilities by leveraging additional data sources.
4 – Extend: Gen AI Models via Fine-Tuning
This approach also extends generative AI models through fine-tuning rather than data retrieval. Fine-tuning adjusts the AI model’s parameters for specific tasks or data sets, providing more customized outputs.
5 – Build: Gen AI Custom Models from Scratch
The most advanced approach involves building custom generative AI models from scratch. This allows complete control over the AI model’s design and capabilities, which can be tailored to unique and specific requirements.
Each approach offers different levels of customization and control, ranging from the simple consumption of pre-built AI tools to the complex development of custom AI models.§
