Hopsworks

What is Hopsworks?

Hopsworks is a modular machine learning platform designed to centralize, manage, and accelerate AI and ML projects through a unified AI Lakehouse that integrates data and model workflows. Its mission is to simplify MLOps by providing pre-integrated tools such as feature stores, model registries, and GPU orchestration, enabling teams to build, share, and deploy ML features and models efficiently. The platform supports real-time feature serving with low latency and scalable pipelines using frameworks like Spark, Flink, and Python. It addresses governance, multi-tenant collaboration, and compliance, suitable for enterprises across sectors like healthcare, finance, retail, and defense. Hopsworks also offers open-source components and flexible deployment options on cloud, hybrid, or on-premises environments.

How to use Hopsworks?

Users start by registering and setting up projects that serve as secure sandboxes to collaborate on ML assets such as features, models, and training data. They use the Hopsworks feature store to engineer and centralize ML features, then build, version, and deploy models with integrated MLOps pipelines leveraging tools like Airflow and KServe. The platform supports GPU workload orchestration for training large models and offers APIs for real-time feature retrieval and model inference. Users can monitor workflows, manage assets, and collaborate efficiently within the platform, enabling faster iteration and production deployment of AI applications.

Hopsworks's Core Features

• Unified AI Lakehouse platform combining data and ML workflows in real time.

• Feature store enabling centralized, versioned, and reusable ML features at scale.

• End-to-end MLOps workflow management including orchestration and monitoring.

• Integrated GPU workload orchestration for efficient deep learning training.

• Model registry and serving with support for KServe and version control.

• Multi-tenant projects for secure collaboration and access control.

• Support for Python, SQL, Spark, Flink, and Jupyter-based feature engineering.

• Low-latency online API and high-throughput offline API for feature retrieval.

• Vector database integration for similarity search based on OpenSearch.

• Platform deployable on cloud, hybrid, on-premises, or air-gapped environments.

Hopsworks's Use Cases

• #1

  Centralizing and reusing ML features to reduce development time

• #2

  Enabling real-time low-latency feature serving for operational ML models

• #3

  Orchestrating end-to-end ML workflows with built-in MLOps tooling

• #4

  Collaborating securely across teams with multi-tenant project management

• #5

  Maximizing GPU utilization for large-scale deep learning training

• #6

  Managing model versions and deployment with a model registry and serving

• #7

  Implementing data governance and compliance in AI pipelines

• #8

  Building scalable AI systems with unified data and model infrastructure