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20

Logical Clocks Webinar

May 27, 2020, 7PM CET

Feature Store
for Kubeflow & On-Premises Clusters (Hadoop)

The Hopsworks Feature Store is a platform for managing and serving features for machine learning that also integrates Kubeflow and On-Premises Clusters (Hadoop). Hopsworks is available as open-source, Enterprise, or as a managed platform on AWS (www.hopsworks.ai)

During this webinar we will introduce the concept of a Feature Store and how it helps manage data for AI. We will walk-through the Hopsworks Feature Store, introducing its concepts and how you can use it from Kubeflow and On-Premises Clusters (Hadoop) for feature engineering, as a feature registry, for creating train/test datasets for ML, and as an online Feature Store to build feature vectors for online applications with low latency.

Speaker

Jim Downling
CEO, Co-Founder and Product Lead @Logical Clocks AB

Jim Dowling is CEO of Logical Clocks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks platform, a horizontally scalable data platform for machine learning that includes the industry’s first Feature Store.
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Hopsworks at a glance

Efficiency & Performance

Development & Operations

Governance & Compliance

Feature Store
Data warehouse for ML
Distributed Deep Learning
Faster with more GPUs
HopsFS
NVMe Speed with Big Data
Horizontally Scalable
Ingestion, Dataprep, training, Serving
Notebooks For development
First-class Python Support
Version Everything
Code, Infrastructure, Data
Model Serving on Kubernetes
TF Serving, MLeap, SkLearn
End-to-End ML Pipelines
Orchestrated by Airflow
Secure Multi-tenancy
Project-based restricted Access
Encription At-rest, In-Motion
TLS/SSL everywhere
AI-Asset Governance
Models, Experiment, data, GPUs
Data/Model/Feature Lineage
Discover/track dependencies

Efficiency & Performance

Feature Store
Data warehouse for ML
Distributed Deep Learning
Faster with more GPUs
HopsFS
NVMe Speed with Big Data
Horizontally Scalable
Ingestion, Dataprep, training, Serving

Development & Operations

Notebooks For development
First-class Python Support
Version Everything
Code, Infrastructure, Data
Model Serving on Kubernetes
TF Serving, MLeap, SkLearn
End-to-End ML Pipelines
Orchestrated by Airflow

Governance & Compliance

Secure Multi-tenancy
Project-based restricted Access
Encription At-rest, In-Motion
TLS/SSL everywhere
AI-Asset Governance
Models, Experiment, data, GPUs
Data/Model/Feature Lineage
Discover/track dependencies