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Search ML Pipelines , found 18 results

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Azure ML Deployer

azure-ml-deployer

jeremylongshore/claude-code-plugins-plus-skills

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

Azure ML Deployer

azure-ml-deployer

jeremylongshore/claude-code-plugins-plus-skills

Automates Azure ML Deployer workflows by offering best-practice guidance, code/config generation, and validation for serving, MLOps pipelines, monitoring, and production optimization.

AutoML Pipeline Builder

building-automl-pipelines

jeremylongshore/claude-code-plugins-plus-skills

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Data Augmentation Pipeline

data-augmentation-pipeline

jeremylongshore/claude-code-plugins-plus-skills

Guides ML training teams through data augmentation pipelines, offering best practices, code/config generation, validation, and experiment-tracking support so pipelines stay production-ready.

Databricks Deployment Automation Bundles

databricks-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

Use Declarative Automation Bundles (DABs) to manage and deploy Databricks assets, including jobs, DLT pipelines, and ML models, using an Infrastructure-as-Code approach. This skill allows defining resources and deployment targets (dev, staging, prod) in a single YAML file, enabling robust CI/CD practices and controlled promotion of changes across environments.

Feature Store Connector

feature-store-connector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up and validating feature store connectors within ML deployment pipelines, covering best practices, code generation, and production monitoring for inference.

Firecrawl Content Ingestion Pipeline Architecture

firecrawl-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive reference architecture details a multi-stage pipeline for web scraping and content ingestion using Firecrawl. It seamlessly integrates single-page scraping, site-wide crawling, URL mapping, and structured data extraction. The subsequent processing layer handles cleaning, deduplication, and chunking, making this blueprint ideal for designing robust knowledge bases, RAG systems, and complex AI data pipelines.

Luigi Task Generator

luigi-task-generator

jeremylongshore/claude-code-plugins-plus-skills

Automates Luigi task generation for data pipelines, offering guided best practices, code and configuration outputs, and validation to streamline ETL, transformation, orchestration, and streaming workflows.

Production ML Engineering

senior-ml-engineer

alirezarezvani/claude-skills

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

Snowflake Data Automation

snowflake-automation

ComposioHQ/awesome-claude-skills

Automates Snowflake data warehouse workflows via the Composio MCP, letting you list databases, schemas, and tables, run SQL (SELECT, DDL, DML), and integrate those operations into cross-app pipelines for faster data ops.

Molecular Featurization Hub

K-Dense-AI/claude-scientific-skills

Molfeat is a Python toolkit that converts SMILES/RDKit molecules into embeddings via 100+ calculators, transformers, and pretrained models for QSAR, screening, similarity search, and deep learning pipelines.

Pysam Genomic Toolkit

K-Dense-AI/claude-scientific-skills

Pysam offers Python bindings to htslib for reading and writing SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, enabling coverage stats, pileup analysis, variant queries, and seamless integration into NGS pipelines.

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