Deployment

Large Language Models (LLMs) deploying on real-world applications presents unique challenges, particularly when it comes to computational resources, latency, and cost-effectiveness. On this comprehensive guide, we'll explore the landscape of LLM serving, with a...

AI hardware is growing quickly, with processing units like CPUs, GPUs, TPUs, and NPUs, each designed for specific computing needs. This variety fuels innovation but in addition brings challenges when deploying AI across different...

2024 is witnessing a remarkable shift within the landscape of generative AI. While cloud-based models like GPT-4 proceed to evolve, running powerful generative AI directly on local devices is becoming increasingly viable and attractive....

What if I told you that you can save 60% or more off of the associated fee of your LLM API spending without compromising on accuracy? Surprisingly, now you may.Large Language Models (LLMs) are...

To gauge the considering of business decision-makers at this crossroads, MIT Technology Review Insights polled 1,000 executives about their current and expected generative AI use cases, implementation barriers, technology strategies, and workforce planning. Combined...

When deploying a model to production, there are two vital inquiries to ask:Should the model return predictions in real time?Could the model be deployed to the cloud?The primary query forces us to choose from...

Machine Learning using DatabricksOn the forefront of the technological revolution, the sports powerhouse Adidas is adapting and leveraging Machine Learning (ML) to weave its magic into myriad business elements. Our highly expert and inventive...

These curves are also useful to find out what threshold we could use in our final application. For instance, whether it is desired to reduce the variety of false positives, then we will select...