Hopsworks
UnclaimedThe unified AI Lakehouse platform for real-time ML feature engineering, deployment, and production.
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TL;DR - Hopsworks
- Provides a unified AI Lakehouse for real-time ML, integrating feature engineering, MLOps, and model serving.
- Features a high-performance Feature Store with sub-millisecond latency powered by RonDB for consistent online/offline data.
- Supports open data formats (Iceberg, Delta, Hudi) and offers flexible deployment options including SaaS, on-premises, and air-gapped.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Achieves sub-millisecond latency for real-time feature retrieval and inference.
- Reduces costs and development time by enabling extensive feature reuse across models.
- Ensures data consistency between training and production environments, mitigating training-serving skew.
- Offers flexible deployment models including SaaS, on-premises, and air-gapped for data sovereignty.
- Integrates directly with open data formats like Iceberg, Delta, and Hudi without requiring data migrations.
Cons
- Requires Kubernetes knowledge for on-premises deployments using the K8s Installer.
- The full benefits of the platform are realized with a comprehensive understanding of ML engineering practices.
Key Features
Feature Store with sub-millisecond retrieval latency (powered by RonDB)AI Lakehouse supporting Iceberg, Delta, and Hudi tablesPython-native query engine for faster data readsMLOps Platform for experiment tracking, model registry, and deployment pipelinesGPU and compute management for LLMs and ML models (with Ray, KServe/vLLM support)Dual Storage System (Online RonDB for serving, Offline Hudi on HopsFS for training)Point-in-Time Correctness for generating historical training datasetsData Lineage and Feature Monitoring for audit trails and drift detection
Pricing Plans
Free
$0
- 1 project included
- Feature Store + Model Registry
- No credit card required
- Community support
- Upgrade anytime
SaaS
Pay as you go
- Unlimited projects
- Feature Store + Model Registry
- Model Serving
- Pay only for what you use
- Community support
- Platform SLA
Enterprise
Contact us
- All SaaS features
- On-premise & air-gapped
- Dedicated support team
- Custom integrations
- SLA guaranteed
About Hopsworks
By Toolradar Team·
Hopsworks is an AI Lakehouse platform designed to streamline the entire machine learning lifecycle, from feature engineering to model deployment and monitoring. It provides a unified environment for managing feature data, training models, and serving predictions with sub-millisecond latency. The platform is built for organizations looking to operationalize AI at scale, offering capabilities like a high-performance Feature Store powered by RonDB, direct integration with open data formats like Iceberg, Delta, and Hudi, and comprehensive MLOps tools.
It caters to data scientists, machine learning engineers, and data engineers who need to build, deploy, and run real-time AI systems. Key benefits include significant cost reduction through feature reuse, faster model deployment, and improved data consistency between training and production environments. Hopsworks supports various deployment options, including SaaS, on-premises, and air-gapped environments, ensuring data sovereignty and flexibility.
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Hopsworks FAQ
How does Hopsworks achieve sub-millisecond latency for feature retrieval?
Hopsworks achieves sub-millisecond latency through its Feature Store, which is powered by RonDB, an in-memory, open-source key-value store with SQL capabilities. This architecture allows for extremely fast lookups of feature vectors, essential for real-time AI applications.
What is the significance of the 'Dual Storage System' in Hopsworks' Feature Store?
The Dual Storage System provides a transparent mechanism for managing feature data. It consists of an Online Store (RonDB) for low-latency serving of the latest feature values and an Offline Store (Apache Hudi tables on HopsFS) for high-bandwidth training and batch inference, including time-travel capabilities for reproducibility. This ensures data consistency across training and serving while optimizing for different access patterns.
Can Hopsworks integrate with existing data lakes built on open formats like Delta Lake or Apache Iceberg?
Yes, Hopsworks is designed to work directly with Delta, Iceberg, and Hudi tables. It features a Python-native query engine that allows users to leverage their existing data lake infrastructure without requiring data migrations or conversions, making it compatible with current data ecosystems.
How does Hopsworks address the challenge of 'training-serving skew' in machine learning models?
Hopsworks addresses training-serving skew by providing a unified Feature Store that ensures the exact same features are used for both model training (from the offline store) and real-time inference (from the online store). This consistency, combined with point-in-time correctness for generating historical datasets, prevents discrepancies that can lead to model failures in production.
What kind of GPU management capabilities does Hopsworks offer for large language models (LLMs)?
Hopsworks provides comprehensive GPU and compute management specifically tailored for LLMs and other ML models. It includes smart scheduling and quota management to maximize GPU utilization and supports training at scale with Ray, and serving with KServe/vLLM, enabling efficient deployment and operation of demanding AI workloads.
What are the deployment options for Hopsworks, particularly for organizations with strict data sovereignty requirements?
Hopsworks offers flexible deployment options including a SaaS Public Preview, on-premises, and air-gapped environments. The 'Sovereign AI' capability ensures full control over data and AI operations, making it suitable for organizations with stringent data sovereignty, security, or compliance requirements.
Source: hopsworks.ai