HopsFS is an open-source scaleout metadata file system, but its primary use case is not Exabyte storage, rather customizable consistent metadata. | Continue reading
HopsFS is an open-source scaleout metadata file system, but its primary use case is not Exabyte storage, rather customizable consistent metadata. | Continue reading
Evolve your models from stateless AI to Total Recall AI with the help of a Feature Store | Continue reading
RonDB enables users to have full control over the assignment of threads to CPUs, how the CPU locking is to be performed and how the thread should be scheduled. | Continue reading
RonDB shows higher availability and the ability to handle larger data sets in comparison with Redis, paving the way to be the fastest key-value store available. | Continue reading
RonDB shows higher availability and the ability to handle larger data sets in comparison with Redis, paving the way to be the fastest key-value store available. | Continue reading
RonDB is a managed key-value store with SQL capabilities. It provides the best low-latency, high throughput, and high availability database available today. | Continue reading
Hopsworks now supports dynamic role-based access control to indexes in elasticsearch with no performance penalty by building on Open Distro for Elasticsearch. | Continue reading
HopsFS-S3: cloud-native distributed hierarchical file system that has the same cost as S3, but has 100X the performance of S3 for file move/rename operations. | Continue reading
Use JOINs for feature reuse to save on infrastructure and the number of feature pipelines needed to maintain models in production. | Continue reading
A data warehouse is an input to the Feature Store. A data warehouse is a single columnar database, while a feature store is implemented as two databases. | Continue reading
Hopsworks supports machine learning experiments to track and distribute ML for free and with a built-in TensorBoard. | Continue reading
Integrate with third-party security standards and take advantage from our project-based multi-tenancy model to host data in one single shared cluster. | Continue reading
Given the increasing interest in feature stores, we share our own experience of building one to help others who are considering following us down the same path. | Continue reading
This article introduces the Hopsworks Feature Store for Databricks, and how it can accelerate and govern your model development and operations on Databricks. | Continue reading
End-to-End ML pipeline with a Feature Store based on MLOps principles | Continue reading
Deep learning: State-of-the-art technique for identifying transactions in AML. Less false positives and higher accuracy than traditional rule-based approaches. | Continue reading
This is a guide to file formats for machine learning in Python. The Feature Store can training/test data in a file format of choice on a file system of choice. | Continue reading
Hopsworks supports easy hyperparameter optimization (both synchronous and asynchronous search), distributed training using PySpark, TensorFlow and GPUs. | Continue reading