Join the Data Phoenix webinar, where Dmytro Spodarets and guests Greg Loughnane (Co-Founder & CEO of AI Makerspace) & Chris Alexiuk (Co-Founder & CTO at AI Makerspace) will discuss how to build an RAG application using fine-tuned domain-adapted embeddings.
We are a fast-growing startup, and we are looking for a Middle+/Senior Machine Learning engineer to join our team.
We are seeking enthusiastic ML Engineers to join our team. This is a great opportunity for those looking to deepen their expertise in machine learning and contribute to various innovative AI projects 🤖
This role involves designing, implementing, and improving retrieval-augmented generation (RAG) and agent LLM-based systems based on private (OpenAI) and open-access (Llama-2, Gemma) models.
NVIDIA's Omniverse Cloud APIs enable developers to integrate RTX rendering and OpenUSD into their software tools and simulation workflows. Industry leaders worldwide leverage the Omniverse Cloud APIs to enhance their workflows and deliver solutions for the development of digital twin simulations.
The newly launched NVIDIA microservices enable organizations to build and deploy AI applications locally while preserving control of their data. NIM supplies high-performing containers for proprietary and open model deployment while CUDA-X accelerates the production process.
NVIDIA announced its new Blackwell architecture at the GTC keynote. Blackwell is the Hopper architecture successor, featuring a 30x performance increase and a 25x cost and energy consumption decrease when training or performing real-time inference for LLMs of up to 10 trillion parameters.
During this talk, we will discuss the Kubernetes stack's drawbacks in the context of AI and show how dstack addresses them in the training and deployment of GenAI models.
Join the Data Phoenix webinar, where Dmytro Spodarets and guest Andrey Cheptsov (Founder & CEO at dstack) will discuss the drawbacks of the Kubernetes stack in the context of AI and show how dstack addresses these drawbacks in the training and deployment of GenAI models.
During this talk, we will discuss a comprehensive understanding of the evaluation processes for LLMs, particularly in the context of preparing these models for deployment in production environments.