Key Takeaways -   To understand data science, one needs a lot of technical expertise along with business understanding. Generative AI, MLOps, and clou ...

These are my go-to libraries for Python data crunching.

While Excel is ubiquitous, I prefer Python for my data analysis. Spreadsheets are great for formatting data, but it's Python that's allowed me to build my own super calculator out of regular Python ...

Vector search underpins most retrieval-augmented generation (RAG) pipelines. At scale, it gets expensive. Storing 10 million document embeddings in float32 consumes 31 GB of RAM. For dev teams running ...

turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...

There’s a lot to know about search intent, from using deep learning to infer search intent by classifying text and breaking down SERP titles using Natural Language Processing (NLP) techniques, to ...

Abstract: Despite many decades of evolution in navigation algorithm techniques, improving the performance of well understood and accepted workhorse algorithms remains an active research area.

PostgreSQL with the pgvector extension allows tables to be used as storage for vectors, each of which is saved as a row. It also allows any number of metadata columns to be added. In an enterprise ...

One of the key components of Microsoft’s Copilot Runtime edge AI development platform for Windows is a new vector search technology, DiskANN (Disk Accelerated Nearest Neighbors). Building on a ...