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Logical Clocks' Blog
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ML Best Practices
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How to Build your own Feature Store
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.
Jim Dowling
May 26, 2020
Hopsworks Feature Store for AWS SageMaker
Integrate AWS SageMaker with Hopsworks to manage, discover and use features for creating training datasets and for serving features to operational models.
Fabio Buso
May 18, 2020
Introducing Hopsworks.ai
Get started with Hopsworks.ai to effortlessly launch and manage Hopsworks clusters in any AWS account that also integrates with Databricks and AWS SageMaker.
Steffen Grohsschmiedt
April 27, 2020
Hopsworks Feature Store for Databricks
This article introduces the Hopsworks Feature Store for Databricks, and how it can accelerate and govern your model development and operations on Databricks.
Fabio Buso
April 23, 2020
ExtremeEarth scales AI to the Earth Observation Community with Hopsworks
How ExtremeEarth Brings Large-scale AI to the Earth Observation Community with Hopsworks, the Data-intensive AI Platform
Theofilos Kakantousis
April 15, 2020
Towards better AI-models in the betting industry with a Feature Store
Introducing the feature store which is a new data science tool for building and deploying better AI models in the gambling and casino business.
Remco Frijling
February 20, 2020
MLOps with a Feature Store
How the Feature Store enables monolithic end-to-end ML pipelines to be decomposed into feature pipelines and model training pipelines.
Fabio Buso
February 14, 2020
Introducing the Hopsworks 1.x series!
The Hopsworks 1.x series has updates to the Feature Store, a new Experiments framework for ML, and connectivity with external systems (Databricks, Sagemaker).
Theofilos Kakantousis
February 17, 2020
AI & Deep Learning for Fraud & AML
Anomaly detection and Deep learning for identifying money laundering . Less false positives and higher accuracy than traditional rule-based approaches.
Jim Dowling
November 27, 2019
Guide to File Formats for Machine Learning: Columnar, Training, and Inferencing
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.
Jim Dowling
October 25, 2019
Hello Asynchronous Search for PySpark
Hopsworks supports easy hyperparameter optimization (both synchronous and asynchronous search), distributed training using PySpark, TensorFlow and GPUs.
Moritz Meister
October 14, 2019
Welcoming AMD/ROCm to Hopsworks
Hopsworks now supports AMD GPUs and ROCm for deep learning, enabling developers to train deep learning models on AMD GPU hardware using TensorFlow.
Robin Andersson
October 14, 2019
Goodbye Horovod, Hello CollectiveAllReduce
Hopsworks is replacing Horovod with Keras/TensorFlow’s new CollectiveAllReduceStrategy , a part of Keras/TensorFlow Estimator framework.
Robin Andersson
October 22, 2018
Introducing Hopsworks
Hopsworks is a data platform that integrates popular platforms for data processing such as Apache Spark, TensorFlow, Hops Hadoop, Kafka, and many others.
Jim Dowling
March 21, 2018
Optimizing GPU utilization in Hops
How we use dynamic executors in PySpark to ensure GPUs are only allocated to executors only when they are training neural networks.
Robin Andersson
September 28, 2018
Millions and millions of files for Deep Learning with HopsFS
Datasets used for deep learning may reach millions of files. The well known image dataset, ImageNet, contains 1m images, and its successor, the Open Images...
Jim Dowling
October 3, 2018
Why you need a Distributed Filesystem for Deep Learning
When you train deep learning models with lots of high quality training data, you can beat state-of-the-art prediction models in a wide array of domains.
Jim Dowling
October 17, 2018
Logical Clocks raises Seed Funding
Today, we are announcing that we have raised €1.25 million in seed funding, led by Inventure with participation by Frontline Ventures and AI Seed.
Jim Dowling
November 20, 2018
When Deep Learning with GPUs, use a Cluster Manager
If you are employing a team of Data Scientists for Deep Learning, a cluster manager to share GPUs between your team will maximize utilization of your GPUs.
Jim Dowling
December 18, 2018
Feature Store: The missing data layer in ML pipelines?
A Feature Store stores features. We go through data management for deep learning and present the first open-source feature store now in Hopsworks' ML Platform.
Kim Hammar
December 30, 2018
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