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Extending Notebook-Centric AI Workflows with Jupyter AI
This article introduces how to extend your Jupyter Notebook environment into a generative AI playground using Jupyter AI. From installation methods to chat interface usage, key slash (/) commands, and %%ai magic commands, we cover the essential features step by step.
No-ETL Data Pipeline Using StarRocks MViews
Discover how to integrate diverse data sources in real-time using StarRocks Materialized Views without complex ETL. This guide covers complete pipeline implementation from data loading to visualization, featuring StarRocks and Apache Iceberg scenarios for cost reduction and high performance.
Deploy StarRocks on Kubernetes
This technical blog provides a hands-on guide for deploying StarRocks distributed analytics database on Kubernetes, covering both Shared-nothing and Shared-data architectures with Helm Charts, MinIO integration, and real-world performance testing to help choose the right deployment mode.
Understanding StarRocks Architecture
StarRocks is an analytical database with a simple FE/BE architecture offering two modes: Shared-nothing (local storage, high performance) and Shared-data (object storage, cost-efficient). It provides excellent MPP performance and seamless horizontal scaling without external dependencies.
Building a Text Curation Pipeline with NeMo Curator
This guide uses NVIDIA NeMo Curator to build high-quality datasets for LLMs. It's a hands-on walk-through of a text curation pipeline—from cleaning and deduplication to language labeling—for preparing large-scale data quickly and reliably.
Evaluating LLMs with NeMo Evaluator: An End-to-End Guide from Standard Benchmarks to Custom Datasets
This guide shows how to use NVIDIA NeMo Evaluator in a PAASUP DIP environment. It covers the end-to-end process of evaluating LLMs connected via an NIM Proxy, using both standard benchmarks and custom data, from setup to result interpretation.
Deploying NVIDIA NIM Microservice in a Private Environment
This guide explains how to securely and efficiently deploy LLM models like llama-3.2-3b-instruct in a private environment using NVIDIA NIM Microservice on PAASUP DIP. It supports both Python code integration and no-code workflows with Flowise, ideal for diverse enterprise AI applications.
Deploying Serverless VLLM in a Private Environment
This is a practical guide for building an enterprise LLM serving environment using KServe with a vLLM backend on the PAASUP DIP platform. It presents various AI service operation methods, from programming through Jupyter Notebook to no-code web interfaces using OpenWebUI and Flowise.
Creating Custom Map Charts in Apache Superset
This guide provides a step-by-step approach to visualizing custom maps in Apache Superset using GIS data.