Skill UI

Provides automated guidance for setting up canary deployments in ML production environments, covering model serving, MLOps pipelines, monitoring, and configuration validation.

Guides ML teams through automated batch inference pipelines, suggesting best practices, monitoring and production readiness, and generating code and configs for deployment.

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.

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

This guide outlines the complete MLOps workflow on Databricks. It covers feature engineering using Feature Store, tracking experiments with MLflow, managing model versions in the Model Registry, and deploying real-time inference endpoints via Mosaic AI Model Serving. Ideal for building robust, production-grade ML pipelines.

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.

Collects Docker build, compose, and runtime tips including multi-stage builds, isolated dependencies, compose healthchecks, .dockerignore, image analysis, and security anti-patterns to streamline container pipelines.

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

This comprehensive guide details advanced techniques to significantly boost Firecrawl scraping speed and efficiency. Learn how to optimize performance by selecting the most efficient output formats (e.g., Markdown over HTML), implementing URL caching, utilizing batch scraping for multiple URLs, and tuning delay parameters for dynamic Single Page Applications (SPAs). Mastering these methods is crucial for building high-throughput, cost-effective web scraping pipelines.

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.

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

Automates Flask-based machine learning API deployment by guiding you through best practices, generating production code/configurations, and validating outputs for serving, monitoring, and MLOps pipelines.