Results for “pipeline”

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

Build multi-agent pipelines with CrewAI — define Agents with roles/goals/backstory, assemble them into a Crew, assign Tasks sequentially or in parallel, and wire LLM + tools per agent.

Generates synthetic CT volumes and masks using NVIDIA's rectified-flow pipeline for medical imaging research.

Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

Builds or modifies a RAG pipeline with intent-based routing, vector store search, citation rendering, and book fidelity enforcement.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

Run standard single-cell RNA-seq analysis pipelines: QC, normalization, dimensionality reduction, clustering, differential expression, and visualization using Scanpy.

Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.

Write, optimize, and debug Apache Spark jobs for high-performance distributed data processing, ETL pipelines, and big data workloads.

Convert books into supervised fine-tuning datasets and train style-transfer models that replicate an author's voice.

Reference for building real-time voice and multimodal AI applications with Pipecat, covering pipelines, speech services, LLM integration, and transports.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

Build real-time voice and multimodal AI applications using Pipecat and Daily, covering pipeline architecture, AI service integration, and transport options.

Write, review, refactor, and optimize Slang shader code for graphics, compute, tessellation, and ray tracing pipelines with cross-compilation support.

Reference for building real-time voice and multimodal AI agents with Pipecat, covering pipelines, speech services, LLMs, transports, and deployment.

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

Reference for building real-time voice and multimodal AI applications with Daily and Pipecat, covering pipeline architecture, AI service integrations, transports, and client SDKs.

Reference for building real-time voice and multimodal AI applications with Pipecat, covering pipelines, speech services, LLMs, transports, and deployment.

Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Build conversational multi-agent systems with AutoGen (AG2) — define AssistantAgent and UserProxyAgent, set up GroupChat with GroupChatManager for round-robin or auto routing, enable code execution, and compose nested chats or sequential pipelines.

Expert skill for building LLM applications with LangChain — LCEL chains, RAG pipelines, agent orchestration, LangGraph integration, LangSmith observability, and production deployment via LangServe. Use when working with LangChain or comparing LLM application frameworks.

Write, review, and migrate code using NVIDIA DALI's imperative dynamic-mode API for efficient data loading and preprocessing.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Automates scientific research workflows from data analysis to publication, orchestrating multiple agents for hypothesis generation, methodology development, computational experiments, and LaTeX paper writing.

Automates genome-wide association studies from genotype files to publication-ready results, running PLINK2 QC and REGENIE regression with Manhattan and QQ plots.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Integrate a HuggingFace computer vision model into the NVIDIA TAO Toolkit ecosystem, covering the full pipeline from prerequisites to container testing.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Maps a described workflow to the right ECC command-group with run-order and stop condition, and browses all command-group recipe families.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies for maximum GPU efficiency.