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Hopsworks Feature Engineering in Python
End-to-End MLOps Management
Hopsworks Operational Performance & High Availability
Hopsworks offers a unified AI Lakehouse platform centered around its Feature Store, enabling real-time AI and efficient MLOps management. It ensures faster development, significant cost reduction, and robust governance through its scalable and flexible deployment options.
Hopsworks positions itself as the AI Lakehouse platform for enterprise-grade MLOps, emphasizing real-time AI, feature management, and sovereign AI deployment. It targets organizations seeking scalable, compliant, and efficient ML infrastructure.
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Customer sentiment appears to be largely positive, as the platform directly addresses key pain points like data inconsistency, inefficient resource utilization, and compliance challenges faced by ML engineers and AI operations leads. The comprehensive feature set and focus on real-time capabilities likely resonate well with technical users.
Clearly communicate the ROI and TCO benefits of the AI Lakehouse approach, especially for mid-sized teams scaling their ML initiatives.
Hopsworks Feature Store and ML platform provides a Python-first, collaborative environment specifically designed for ML Engineers, Data Engineers, and Data Scientists. It facilitates the entire machine learning lifecycle, with a strong emphasis on feature engineering, management, and serving. This platform aims to streamline the process of creating, sharing, and deploying features for ML models, ensuring consistency and reusability.
Hopsworks is the flexible and modular AI Lakehouse with a feature store that provides seamless integration for existing pipelines, superior performance for ...
View sourceBut i'm not sure where to upload that csv. Secondly, when i ingested some data manually into df and tried creating featuregroup on top of it. It is throwing ...
View sourceSep 13, 2023 ... In this article, we present a new mental map for ML Systems as three independent ML pipelines: feature pipelines, training pipelines, and inference pipelines.
View sourceThe current assets that can be shared between projects are: files/directories in HopsFS, Hive databases, feature stores, and Kafka topics. Important. Sharing ...
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