Plugins
5 pluginscurated
Project Verification Pipeline
For developers running comprehensive verification pipelines for Laravel or Quarkus projects before PRs or releases.
4 skills · plugin
curated
DotNet Test Migration to MTP
Migrate .NET test projects from VSTest to MTP, updating project files, CLI, and CI/CD pipelines.
3 skills · plugin
curated
FP-TS Backend Toolkit
For Node.js/Deno developers building type-safe backends with fp-ts, covering async pipelines and service composition.
3 skills · plugin
@dotnet
Dotnet AI
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
5 skills · plugin
curated
Deploy Azure ML Pipeline
Manage Azure Machine Learning resources including workspaces, jobs, models, data, compute, and pipelines using the SDK v2 for Python.
3 skills · plugin
Results for “pipelines”
16 skills
nimoqup046-collab AI Ml
Orchestrates AI/ML development workflows covering LLM applications, RAG systems, AI agents, ML pipelines, and observability.
2
lucaspmarie-a11y AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
5
affaan-m Pytorch Patterns
Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.
226k
neuralblitz Tensorflow
Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.
1
lingxling AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
253
jorcan AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
0 · bundle
More results
phoroth AI Ml
Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.
3
k-dense-ai Histolab
Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.
30.2k · bundle
jeffallan Ml Pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
jorcan LLM Ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
oyi77 RAG Builder
Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.
10
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
nvidia Nemotron Customize
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.
2.2k · bundle
k-dense-ai Pyhealth
Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.
30.2k · bundle
qhjqhj00 Ray Data
Process large ML datasets in parallel across CPU or GPU clusters, with streaming execution, multi-format I/O, and integration with Ray Train, PyTorch, and TensorFlow for batch inference and preprocessing pipelines.
3 · bundle
k-dense-ai Pytorch Lightning
Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.
30.2k · bundle