Results for “pipeline”

Manage Azure Machine Learning resources including workspaces, jobs, models, data, compute, and pipelines using the SDK v2 for Python.

Convert browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase, replacing opaque agent loops with deterministic pipelines where possible.

Plan, configure, and chain Nemotron model customization steps into single-step or multi-step pipelines for curation, translation, fine-tuning, RL alignment, benchmarking, checkpoint conversion, optimization, and evaluation.

Encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, and build multi-step media processing pipelines using Transloadit's cloud infrastructure.

Build synthetic datasets and data generation pipelines using the Data Designer library.

Generates CI/CD pipeline configurations for Adobe App Builder projects, including GitHub Actions workflows, Azure DevOps, and GitLab CI templates with OAuth S2S secrets injection and multi-workspace promotion.

Build synthetic datasets and data generation pipelines using the Data Designer library.

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

Build automated evaluation pipelines for Python LLM applications using real LLM calls and structured test datasets.

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

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

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

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.

Provides file paths to language-specific extension files for the code-testing pipeline, enabling discovery of guidance for .NET, Python, TypeScript, and other languages.

Provides structured prompts for analyzing, planning, and executing .NET framework upgrades, covering project assessment, dependency management, code modernization, CI/CD updates, testing, and documentation.

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

Authenticate Java applications with Azure services using Microsoft Entra ID, supporting credential types for local development, managed identities, service principals, and CI/CD pipelines.

Generates and writes unit tests for any programming language using a multi-agent pipeline that researches, plans, and implements tests with build and verification steps.

Orchestrates a dimensional-analysis pipeline to annotate codebases with unit/dimension comments, discover dimensional vocabulary, and detect arithmetic bugs from unit mismatches or precision loss.

Coordinate the end-to-end pipeline from CAD or source assets to SimReady USD assets, including conversion, material and physics assignment, validation, conformance, and optional packaging.

Generates phrase-grounded bounding box annotations from image-caption pairs using a VLM, producing cleaned captions, referring expressions, and pixel-space bounding boxes.

Migrates .NET test projects from VSTest to Microsoft.Testing.Platform (MTP), updating project files, CLI arguments, and CI/CD pipelines for MSTest, NUnit, and xUnit.net.

Guided multi-page redesign of an existing website through a four-phase pipeline — extract, direct, prototype, and migrate. Tracks progress incrementally per page so redesigns are resumable.

Integrate Xquik into apps, scripts, data pipelines, or AI agents for X API tasks like tweet search, user lookup, follower export, media actions, and webhook verification.

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA, including picking the right base pipeline, designing a tailored UI, and publishing the Space.

Build video analytics pipelines using NVIDIA DeepStream SDK 9.0 with Python pyservicemaker API, including GStreamer-based video processing, TensorRT inference integration, object detection/tracking, and Kafka/message broker integration.

Generates synthetic body MRI volumes using NVIDIA's NV-Generate-CTMR rflow-mr model. Wraps the upstream diffusion inference pipeline with config staging, output validation, and NIfTI volume summarization.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.