Hopsworks is a data platform for ML with a Python-centric Feature Store and MLOps capabilities. Hopsworks is a modular platform. You can use it as a standalone Feature Store, you can use it to manage, govern, and serve your models, and you can even use it to develop and operate feature pipelines and training pipelines. Hopsworks brings collaboration for ML teams, providing a secure, governed platform for developing, managing, and sharing ML assets - features, models, training data, batch scoring data, logs, and more.
→ Go to app.hopsworks.ai
Hopsworks is available as a serverless app, simply head to app.hopsworks.ai and register with your Gmail or Github accounts. You will then be able to run a tutorial or access Hopsworks directly and try yourself. This is the prefered way to first experience the platform before diving into more advanced uses and installation requirements.
Managed Hopsworks is our platform for running Hopsworks and the Feature Store in the cloud and integrates directly with the customer AWS/Azure/GCP environment. It also integrates seamlessly with third party platforms such as Databricks, SageMaker and KubeFlow.
If you wish to run Hopsworks on your Azure, AWS or GCP environement, follow one of the following guides in our documentation:
It is possible to use Hopsworks on-premises, which means that companies can run their machine learning workloads on their own hardware and infrastructure, rather than relying on a cloud provider. This can provide greater flexibility, control, and cost savings, as well as enabling companies to meet specific compliance and security requirements.
Working on-premises with Hopsworks typically involves collaboration with the Hopsworks engineering teams, as each infrastructure is unique and requires a tailored approach to deployment and configuration. The process begins with an assessment of the company's existing infrastructure and requirements, including network topology, security policies, and hardware specifications.
For further details about on-premise installations: contact us.
You need at least one server or virtual machine on which Hopsworks will be installed with at least the following specification:
- Centos/RHEL 8.x or Ubuntu 22.04;
- at least 32GB RAM,
- at least 8 CPUs,
- 100 GB of free hard-disk space,
- a UNIX user account with sudo privileges.
Hopsworks documentation includes user guides, feature store documentation and an administration guide. We also include concepts to help user navigates the abstractions and logics of the feature stores and MLOps in general:
- Feature Store: https://docs.hopsworks.ai/3.0/concepts/fs/
- Projects: https://docs.hopsworks.ai/3.0/concepts/projects/governance/
- MLOps: https://docs.hopsworks.ai/3.0/concepts/mlops/prediction_services/
Hopsworks API documentation is divided in 3 categories; Hopsworks API covers project level APIs, Feature Store API covers covers feature groups, feature views and connectors, and finally MLOps API covers Model Registry, serving and deployment.
- Hopsworks API - https://docs.hopsworks.ai/hopsworks-api/3.0.1/generated/api/connection/
- Feature Store API - https://docs.hopsworks.ai/feature-store-api/3.0.0/generated/api/connection_api/
- MLOps API - https://docs.hopsworks.ai/machine-learning-api/3.0.0/generated/connection_api/
Most of the tutorials require you to have at least an account on app.hopsworks.ai. You can explore the dedicated https://github.com/logicalclocks/hopsworks-tutorials repository containing our tutorials or jump directly in one of the existing use cases:
- Fraud (batch): https://github.com/logicalclocks/hopsworks-tutorials/tree/master/fraud_batch
- Fraud (online): https://github.com/logicalclocks/hopsworks-tutorials/tree/master/fraud_online
- Churn prediction https://github.com/logicalclocks/hopsworks-tutorials/tree/master/churn
Hopsworks provides projects as a secure sandbox in which teams can collaborate and share ML assets. Hopsworks' unique multi-tenant project model even enables sensitive data to be stored in a shared cluster, while still providing fine-grained sharing capabilities for ML assets across project boundaries. Projects can be used to structure teams so that they have end-to-end responsibility from raw data to managed features and models. Projects can also be used to create development, staging, and production environments for data teams. All ML assets support versioning, lineage, and provenance provide all Hopsworks users with a complete view of the MLOps life cycle, from feature engineering through model serving.
Hopsworks provides development tools for Data Science, including conda environments for Python, Jupyter notebooks, jobs, or even notebooks as jobs. You can build production pipelines with the bundled Airflow, and even run ML training pipelines with GPUs in notebooks on Airflow. You can train models on as many GPUs as are installed in a Hopsworks cluster and easily share them among users. You can also run Spark, Spark Streaming, or Flink programs on Hopsworks, with support for elastic workers in the cloud (add/remove workers dynamically).
Hopsworks is available as a both managed platform in the cloud on AWS, Azure, and GCP, and can be installed on any Linux-based virtual machines (Ubuntu/Redhat compatible), even in air-gapped data centers. Hopsworks is also available as a serverless platform that manages and serves both your features and models.
We are building the most complete and modular ML platform available in the market, and we count on your support to continuously improve Hopsworks. Feel free to give us suggestions, report bugs and add features to our library anytime.
- Ask questions and give us feedback in the Hopsworks Community
- Join our Public Slack Channel
- Follow us on Twitter
- Check out all our latest product releases
Hopsworks is available under the AGPL-V3 license. In plain English this means that you are free to use Hopsworks and even build paid services on it, but if you modify the source code, you should also release back your changes and any systems built around it as AGPL-V3.
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