Question 1

What is an AI Lakehouse?

Accepted Answer

An AI Lakehouse is an architecture that combines data lakes and data warehouses to support AI and ML workloads. It extends the traditional lakehouse by adding components for AI/ML such as an Online Store for real-time feature serving and a Vector Index for similarity search. With Hopsworks, you can keep your data where it is (supporting Delta, Iceberg, and Hudi) while building AI applications on top.

Question 2

What is a Feature Store?

Accepted Answer

A Feature Store is a data platform for managing the storage and serving of features used in ML models. The Hopsworks Feature Store provides a single source of truth for all your feature data, with sub-millisecond latency for real-time serving powered by RonDB. It eliminates training-serving skew by using the same features for both training and inference.

Question 3

How does the Feature Store fit into MLOps?

Accepted Answer

The Feature Store is the data layer that connects your feature pipelines, training pipelines, and inference pipelines. It enables teams to build features once and reuse them across models, track data lineage for governance, and ensure point-in-time correct training data without data leakage.

Question 4

Can I try Hopsworks for free?

Accepted Answer

Yes. Our SaaS platform at run.hopsworks.ai is pay-as-you-go with no upfront commitment. You can also deploy on your own Kubernetes cluster with a free evaluation license using our K8s installer.

Question 5

What tools and platforms does Hopsworks integrate with?

Accepted Answer

Hopsworks integrates with 30+ tools including TensorFlow, PyTorch, Spark, Flink, Airflow, Databricks, Snowflake, BigQuery, SageMaker, and more. You can connect to your existing data lakes, warehouses, and orchestration tools.

Question 6

Where can I deploy Hopsworks?

Accepted Answer

Hopsworks runs on AWS, GCP, Azure, OVHcloud, or on-premise with Kubernetes. You can use our managed SaaS platform, deploy on your own cluster with our Helm-based K8s installer, or run air-gapped for enterprise requirements.

Question 7

What is real-time feature serving?

Accepted Answer

Real-time feature serving retrieves precomputed features with sub-millisecond latency for online ML models. When a user action triggers a prediction, Hopsworks returns the feature vector instantly using RonDB, our real-time database. This provides the history and context that stateless online models need.

Question 8

How does Hopsworks prevent training-serving skew?

Accepted Answer

Training-serving skew occurs when features are computed differently during training vs. inference. Hopsworks prevents this through a unified online/offline store that uses the same feature definitions everywhere, and by managing model-dependent transformations that are applied consistently in both training and inference pipelines.

Question 9

Who benefits from using Hopsworks?

Accepted Answer

Data scientists use Hopsworks to find and reuse existing features, reducing development time. Data engineers build and operate feature pipelines that feed multiple models. ML engineers deploy models with consistent feature access. Organizations benefit from feature reuse across teams, reduced data duplication, and faster time to production.

Question 10

Is Hopsworks only for large organizations?

Accepted Answer

No. Our SaaS platform is pay-as-you-go, so small teams can start with no infrastructure to manage and pay only for what they use. As you scale, Hopsworks grows with you. Large organizations benefit from multi-tenant isolation, governance, and collaboration features.

Question 11

What about data quality and monitoring?

Accepted Answer

Hopsworks includes built-in data validation using Great Expectations, and feature monitoring to detect drift and data quality issues. You can define expectations, trigger alerts, and monitor feature distributions over time.

Question 12

Does Hopsworks offer technical support?

Accepted Answer

Enterprise customers get tiered support with dedicated teams. Everyone can access our community forum, public Slack, and documentation. Contact us to discuss enterprise support.

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