By default, it uses Milvus Lite with a local .db file — no server needed. For production, switch to Milvus standalone/cluster or Zilliz Cloud. - Source: dev.to / 3 months ago

As an engineer designing real-time RAG pipelines, I consistently face the challenge of selecting infrastructure capable of handling massive vector datasets without compromising latency or reliability. My recent evaluation of Zilliz Cloud deployed on AWS revealed several architecturally significant patterns worth sharing. - Source: dev.to / 11 months ago

As an engineer managing AI workloads, I’ve learned that observability isn’t optional—it’s survival gear. When my team adopted Zilliz Cloud for vector search in our RAG pipeline, we needed granular visibility into latency, memory, and throughput. Prometheus emerged as the logical choice, but integration reveals subtle pitfalls. Here’s what I discovered deploying this stack. - Source: dev.to / about 1 year ago

As an engineer scaling semantic search systems, I’ve learned that observability separates functional prototypes from production-grade AI. Last quarter, I hit critical bottlenecks in our retrieval-augmented generation pipeline when QPS spiked unexpectedly. The core issue? Our monitoring couldn’t correlate Milvus-based vector search latency with downstream LLM inference. That’s when I integrated Zilliz Cloud’s... - Source: dev.to / about 1 year ago

Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. - Source: dev.to / over 1 year ago

The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / over 1 year ago

That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / about 2 years ago

Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions that allow you to train and use machine learning models with SQL. Here's how you can train a classification model for the classic handwritten digit recognition problem:. - Source: dev.to / over 2 years ago

PostgresML | You know Postgres. Now you know machine learning – PostgresML. - Source: dev.to / over 2 years ago

You can swap in almost any open-source model on Huggingface. HuggingFaceH4/zephyr-7b-beta, Gryphe/MythoMax-L2-13b, teknium/OpenHermes-2.5-Mistral-7B and more.If you haven't seen us here before, we're PostgresML, an open-source MLOps platform built on Postgres. We bring ML to the database rather than the other way around. Source: over 2 years ago