{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/9"}],"enrichment":{"capability":"Python SDK for connecting to Hopsworks clusters to manage feature stores, model registries, and model serving deployments.","skillfed_tags":["feature-store","mlops","model-registry"],"use_cases":["Build and manage a centralized feature repository: create feature groups from dataframes, version them, and share features across multiple ML projects.","Generate training datasets: query feature groups, join them, and export as CSV, Parquet, or TFRecord for model training pipelines.","Serve real-time predictions: fetch online feature vectors on demand (combining stored features with request-time context) to feed into deployed models.","Register and deploy ML models: version TensorFlow, PyTorch, or other models in the registry, then deploy them as HTTP endpoints for inference.","Integrate with cloud storage: use storage connectors to ingest data from S3, GCS, or other sources into the feature store.","Monitor and manage Hopsworks infrastructure: on self-hosted clusters, manage environments, Kafka topics, and job executions via the API."],"what_it_does":"Hopsworks is a Python client library for the Hopsworks platform, an MLOps and DataOps system centered on a feature store. It lets you connect to a Hopsworks cluster (cloud-hosted or self-managed) to manage features, training datasets, ML models, and real-time serving. The library supports two operational modes: Spark mode for large-scale data engineering jobs that write features into the store, and Python mode for data science workflows that read features, build training datasets, and deploy models for inference.\n\nThe package wraps three main APIs: the Feature Store API (create feature groups, insert dataframes, build training datasets, serve real-time feature vectors), the Model Registry API (register and version ML models), and the Model Serving API (deploy and manage model endpoints). It depends on 24 runtime packages including pandas, numpy, boto3, grpcio, and opensearch-py to handle data processing, cloud storage, gRPC communication, and search operations. Most workflows begin with `hopsworks.login()` to authenticate against your cluster, then branch into feature store, model registry, or serving operations.","worth_installing":"Yes, if you are building ML systems on Hopsworks. The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive Apache-2.0 license. Its 24 dependencies are substantial but necessary for the breadth of integrations (cloud storage, databases, gRPC, search). Suitable for teams already committed to the Hopsworks platform; not a general-purpose feature store library for other backends."},"id":"hopsworks","links":{"html":"https://skillfed.io/packages/hopsworks","md":"https://skillfed.io/packages/hopsworks.md","pypi":"https://pypi.org/project/hopsworks/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":null,"license_treatment":"permissive","name":"hopsworks","python_support":"supports_current","summary":"Hopsworks Python SDK to interact with Hopsworks Platform, Feature Store, Model Registry and Model Serving"},"popularity":{"monthly_downloads":148657,"position":11024,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.0.4"}