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Plugins

1 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 “data-pipelines”

9 skills
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
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
More results
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
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
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
Histolab
Process whole slide images for digital pathology: detect tissue, extract tiles, and prepare datasets for deep learning pipelines.
30.2k · 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
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