📌 Case study: how Uber balances open-source and in-house LLM training
AI case studies: November 2024 | How AI is transforming the world?
📌 Case study: The hybrid approach, how Uber balances open-source and in-house LLM training
In the rapidly evolving landscape of generative AI, businesses face a critical decision: rely solely on external providers or invest in building in-house LLM capabilities.
Uber, the well known global leader in mobility and delivery, has opted for a strategic hybrid approach.
This approach combines the strengths of both open-source and closed-source LLMs, enabling Uber to tailor its AI solutions to its diverse business needs while maintaining flexibility and control.By leveraging open-source models, Uber can benefit from the rapid advancements and collaborative development efforts within the AI community.
This allows Uber to experiment with different architectures and techniques without vendor lock-in constraints.
Additionally, open-source models often offer a more cost-effective option than proprietary models, enabling Uber to allocate resources more efficiently.
On the other hand, closed-source models can provide access to cutting-edge technology and specialized expertise.
These models may be pre-trained on massive datasets and fine-tuned for specific tasks, offering a more polished and ready-to-use solution.
By incorporating both open-source and closed-source models, Uber can strike a balance between innovation and efficiency.
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