{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/10"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/3"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Code Generators","url":"https://skillfed.io/packages/category/software-development-code-generators"}],"enrichment":{"capability":"Chalk is a Python SDK for building and deploying machine learning feature pipelines that compute features from multiple data sources and serve them for both online inference and offline training.","skillfed_tags":["feature-store","ml-infrastructure","data-pipeline"],"use_cases":["Define and deploy feature pipelines that compute user credit scores, fraud signals, or recommendation features from multiple databases and APIs.","Build training datasets with temporal consistency\u2014retrieve historical feature values as they existed at specific past timestamps for model training.","Serve low-latency features to production models via REST API with declarative caching and staleness policies to balance freshness and cost.","Ingest batch data from data warehouses on a schedule and automatically make it available for both offline analysis and online serving.","Integrate vendor APIs and expensive external data sources with caching to reduce latency and per-call costs in online inference.","Track model provenance and dataset lineage by storing and versioning feature computations alongside training data."],"what_it_does":"Chalk is a feature engineering framework that lets you define machine learning features as Python functions and automatically orchestrates them into pipelines. You write resolvers\u2014decorated functions that compute features from SQL, REST APIs, data warehouses, or other sources\u2014and Chalk handles the orchestration, caching, scheduling, and deployment. It supports both online serving (low-latency feature queries for inference) and offline training (temporally-consistent historical feature sets for model development).\n\nThe package integrates with standard Python libraries (pydantic for schemas, requests for HTTP, SQL connectors for databases) and uses a Rust-based execution engine underneath for performance. It provides declarative caching policies, scheduled batch ingestion, reverse ETL to bring slow data sources online, and a REST API for querying features in production. The core idea is that the same source code serves both training and inference, ensuring feature consistency and reducing development overhead.","worth_installing":"Yes, with conditions. Chalk is actively maintained and has low install friction. It solves a real problem\u2014coordinating feature computation across multiple data sources for both training and serving\u2014and integrates well with standard Python ML tools. However, the license is unclear, so verify terms before production use. Also confirm whether your use case aligns with Chalk's architecture. If you need a feature store with unified online/offline semantics and don't have license concerns, it's worth evaluating."},"id":"chalkpy","links":{"html":"https://skillfed.io/packages/chalkpy","md":"https://skillfed.io/packages/chalkpy.md","pypi":"https://pypi.org/project/chalkpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"unclear","name":"chalkpy","python_support":"supports_current","summary":"Python SDK for Chalk"},"popularity":{"monthly_downloads":621410,"position":5715,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.154.36"}