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Specialised models perform specific tasks efficiently, reduce costs and energy consumption, and ensure sustainable AI integration.
size, complexity, and training data to meet business needs without incurring unnecessary resource use.
Contrary to the belief that bigger is always better, smaller, specialised models often perform specific tasks more efficiently. This strategic approach not only reduces costs and energy consumption but also ensures sustainable AI integration.
To address these issues, channel partners can follow a structured roadmap:
# 1 Define business objectives and use cases
The initial and most crucial step involves close collaboration with enterprise customers to clearly identify the specific business problems that LLMs are intended to solve and to define the desired outcomes and measurable KPIs for AI implementation. The focus should always be on the value proposition and the unique needs of the client rather than simply adopting the latest technology for its own sake.
# 2 Assess data requirements and quality
Channel partners need to guide their enterprise customers in evaluating the availability, accessibility, quality, and relevance of the data that will be necessary for either fine-tuning existing LLMs or for informing the selection of appropriate pretrained models. This includes emphasising the importance of data
Key takeaways
• Integrating LLMs into enterprise IT poses significant challenges and channel partners struggle with this.
• A critical success factor is right-sizing, selecting LLMs of optimal size and configuration to match business needs.
• At the heart of the AI revolution are deep LLMs, trained on vast datasets, often with billions or trillions of parameters.
• LLMs are based on transformer networks, allows them to perform natural language tasks such as generation, translation, sentiment analysis.
• While scale contributes to capabilities, it also brings challenges in terms of computational resources for training and deployment.
• In contrast to large models are micro LLMs, or small language models, which are specialised models for particular domains or tasks.
• Micro LLMs, fine-tuned versions of larger models, align closely with industry and customer needs.
• Micro LLMs offer advantages like improved domain accuracy, lower computational costs, reduced latency, enhanced data privacy.
• Utilising a buy-and-build strategy, combining foundational models with smaller models, can deliver rapid implementation and lower risks.
• LLMs can be categorised based on their training and use cases, including generalpurpose, instruction-tuned, and dialog-tuned models.
• Concept of right-sizing LLMs is paramount for enterprises aiming to leverage this technology.
• Right-sizing involves selecting models that balance size, complexity, training data to meet business needs.
• Contrary to the belief that bigger is better, smaller, specialised models perform specific tasks more efficiently.
• This approach reduces energy consumption and ensures sustainable AI integration.
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