Demystifying Large Language Models:
There is no escaping the excitement and potential that generative AI has been commanding recently? particularly with regard to large language models (LLMs). The release of GPT-4 in March 2023 has produced a strong gravitational pull? resulting in enterprises making clear and intentional moves toward the adoption of the latest LLM technology.
As a result? other technology companies have increased their investments and efforts to capitalize on large language models’ potential? resulting in the release of LLMs from Microsoft? Google? Hugging Face? NVIDIA? and Meta? to name a few.
The rush by enterprises to adopt
Deploy large language models to production should be netherlands whatsapp number data tempered with the same due diligence that is applied to other technology implementations. We have seen some unfortunate and very public issues with LLM adoption exposing sensitive internal intellectual property? as well as governmental actions putting the brakes on adoption.
In this article? we will be looking into how enterprises can overcome the challenges associated with large language models’ deployment and produce desired business outcomes. We’ll look at some common myths surrounding LLM deployment and address misconceptions? such as? “The bigger the model? the better?” and? “One model will do everything.” We’ll also explore there’s a lot involved in creating best practices in LLM deployment? focusing on key areas such as model deployment? optimization? and inferencing.
Challenges to be considered with large language models enterprise deployments fall into some of the following categories:
Complex engineering overhead to deploy custom models within your own secure environment
Infrastructure availability can be a blocker (e.g.? GPUs)
High inferencing costs as you scale
Long time to value/ROI
It’s one thing to experiment with large language atb directory models that are trained on public data? versus training and operationalizing LLMs on your enterprise data within the constraints of your environment and market.
