A Quick Overview of Nebari
A Collaboration-minded AI Platform for Data and Science.
Simplifying Software Packaging for Scalable, Reproducible Development
From development to production, Quansight ensures stability and scalability across environments.
Building Private On-Prem AI Infrastructure
There are many reasons to keep your AI infrastructure on-prem. These range from cost concerns (GPUs in the cloud get expensive fast) to organizational policies to data privacy and regulatory concerns
My NumPy Year: Creating a DType for the Next Generation of Scientific Computing
First, I’ll start with a brief history of strings in NumPy to explain how strings worked before NumPy 2.0 and why it was a little bit broken.
Easy AI Computational Benchmarking Across Multiple Cloud Resources
Determining the most efficient cloud hardware for training, evaluating, or deploying a deep learning model can be time-consuming, and if the model runs on poorly chosen resources, the cost can be high. Historically, benchmarking AI model computational performance required sophisticated infrastructure or expensive SAAS products, which are often out of reach for teams without dedicated DevOps expertise or deep pockets.
A Year in Review: Quansight’s Contributions to PyTorch in 2023 (& Early 2024)
2023 will be remembered as the year when AI and LLMs took the world by storm. PyTorch took center stage during this revolution due to the rise of torch.compile. The combination of having a fully flexible eager execution model, paired with a compiler with a rather flexible tracer that is able to understand complex Python programs semantically, has certainly been one of the core components fueling these advances.
Make Your AI Vision a Reality with Quansight’s AI Engineering Consulting
Artificial Intelligence (AI) is reshaping industries worldwide, driving innovation and pioneering new possibilities. The journey from AI research to practical, enterprise-ready solutions can be challenging, and this is where Quansight’s AI Engineering consulting services come into play. With our deep expertise in open source scientific computing
Ragna in Action: Building AI Document Interrogation Apps with Open Source Tools
A look at recent presentations on AI, RAG, and Ragna by Quansight’s staff. “For playing around, Generative AI is definitely cool. For asking serious questions about documents, which you’re going to base your business on, I think you still need to do your research and work out how to put some guard rails on it.” […]
Building a GPU CI Service for conda-forge
The recent revision of the array API standard marks another major milestone in the collective effort to achieve array interoperability across the Python data ecosystem.
A recent post by Quansight Lab’s Athan Reines on the Data APIs blog shares updates on the consortium’s progress and plans for the future.
Introducing Lightweight Versions of GDAL and PDAL
Geocoding is the practice of taking in an address and assigning a latitude-longitude coordinate to it. Doing so for millions of rows can be an expensive and slow process, as it typically relies on paid API services. Learn how we saved our client time and money by leveraging open source tools and datasets for their geocoding needs.
