PAASUP IDEAS (Page 2)

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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.

Fine-Tuning and Evaluation of LLMs with NeMo Microservices: A Practical Tutorial

In this guide, we cover a hands-on workflow that leverages NVIDIA NeMo Microservices in the PAASUP DIP environment to fine-tune the llama-3.2-3b-instruct model with KoAlpaca data using LoRA, and directly compare performance before and after tuning.

LLM Fine-Tuning with NeMo Framework: A SQuAD Evaluation Tutorial

Discover how to fine-tune LLaMA 3 8B using NVIDIA NeMo Framework with LoRA-based PEFT and evaluate performance on the SQuAD dataset. See dramatic results: LoRA fine-tuning achieved 81% improvement in Exact Match over the base model, providing practical insights for enterprise QA system development.

NeMo Framework and NeMo Microservices: A Comprehensive Overview

Comprehensive guide to NVIDIA NeMo platform supporting the entire AI model lifecycle from development to deployment. Discover the differences between research-focused NeMo Framework and enterprise-grade NeMo Microservices, plus real-world implementation in PaaS-UP DIP platform.

Modern Data Pipeline Implementation with Paasup DIP: Framework Application and ELT Monitoring Dashboard #4

Discover how PaaS-UP DIP team's spark-batch framework achieves 50% development productivity improvement with ELT monitoring dashboard implementation. Experience dramatically reduced code complexity compared to pure libraries and real-time operational monitoring system built on Apache Superset.

Modern Data Pipeline Implementation with PAASUP DIP: Subway User Statistics Analysis Project #3

Urban traffic pattern analysis project processing over 10 years of Seoul subway ridership data using modern data lake technology stack. Transformed 73,692 records with Delta Lake and Apache Superset to visualize rush hour commute patterns and transfer station hub effects.

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PAASUP IDEAS

PAASUP IDEAS is our technical blog where we share cutting-edge data intelligence technologies, industry trends, experimental findings, and practical use cases of PAASUP DIP. Join us to explore expert

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