Why Developers and AI Engineers Are Embracing Private Tailored SLMs?

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Mahesh Chand · Answer

The future is private, tailored SLMs because public LLMs have the whole world's data, and it doesn't make sense to query and execute so much behind the scenes while your focus is only on one specific area. Let's take an example. OpenAI's LLM has data from all over the world. They have copied everything they can find on the internet and more. Now, if all I want to build is an app, say Sharp-GPT.ai, that is related to programming only, I don't need the entire world's data. All I need is data that is related to programming. For example, GitHub Copilot only provides programming and software development solutions. Suppose you want to create a chatbot for banking. All you will need is banking-related data. This saves tons of processing and resource consumption and makes queries much faster. Privately tailored SMLs are also private and tailored to your business needs, as the name suggests, which means your data will not be available to the public.

Daniel Wright · Answer

Developers and AI engineers are increasingly turning to Private Tailored Service Level Models (SLMs) for a variety of reasons. One key motivation is the need for customized and fine-tuned performance metrics that align closely with the specific requirements of their applications. By tailoring SLMs, professionals can define and track metrics that are most pertinent to their projects, enhancing overall accuracy and efficiency. Moreover, Private Tailored SLMs offer a higher degree of control and flexibility compared to standard service level agreements (SLAs) or generic models. Developers can adapt these models to match the dynamic nature of their applications, enabling them to better manage resources and optimize performance in real-time. Another important aspect is data privacy and security. By leveraging Private Tailored SLMs, developers can ensure that sensitive data remains within their own infrastructure, addressing concerns related to compliance, confidentiality, and data protection regulations. Furthermore, these customized models empower developers to track and improve specific performance indicators that are crucial for the success of their AI applications. Whether it's reducing latency, increasing throughput, or enhancing accuracy, Private Tailored SLMs enable developers to fine-tune their models for optimal performance. In practical terms, this approach allows developers and AI engineers to build more robust and efficient AI systems that are tailored to their unique needs and specifications. By embracing Private Tailored SLMs, professionals can unleash the full potential of their applications while maintaining a high level of control and security over their data and operations.

Why Developers and AI Engineers Are Embracing Private Tailored SLMs?

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