Logical Clocks Blog - Machine and Deep Learning Blog

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ROI of Feature Stores

This blog analyses the cost-benefits of Feature Stores for Machine Learning and estimates your return on investment with our Feature Store ROI Calculator.

How we secure your data with Hopsworks

Integrate with third-party security standards and take advantage from our project-based multi-tenancy model to host data in one single shared cluster.

Antonios Kouzoupis

Software engineer

Beyond Self-Driving Cars: Four Use Cases of Machine Learning in the Automotive Industry

Feature store as a new element and AI tool in the machine learning systems for the automotive industry.

Unifying Single-host and Distributed Machine Learning with Maggy

Try out Maggy for hyperparameter optimization or ablation studies now on Hopsworks.ai to access a new way of writing machine learning applications.

Software Engineer

Manage your own Feature Store on Kubeflow with Hopsworks

Learn how to integrate Kubeflow with Hopsworks and take advantage of its Feature Store and scale-out deep learning capabilities.

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.

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.

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

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.

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

February 20, 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.

February 14, 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.

February 17, 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

November 27, 2019

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.

October 25, 2019

Guide to File Formats for Machine Learning: Columnar, Training, and Inferencing

This is a guide to file formats for ml in Python. The Feature Store can store training/test data in a file format of choice on a file system of choice.

October 14, 2019

Hello Asynchronous Search for PySpark

Hopsworks supports easy hyperparameter optimization (both synchronous and asynchronous search), distributed training using PySpark, TensorFlow and GPUs.

Software Engineer

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

Software engineer

October 22, 2018

Goodbye Horovod, Hello CollectiveAllReduce

Hopsworks is replacing Horovod with Keras/TensorFlow’s new CollectiveAllReduceStrategy , a part of Keras/TensorFlow Estimator framework.

Robin Andersson

Software engineer

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.

September 28, 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

Software engineer

October 3, 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...

October 17, 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.

November 20, 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.

December 18, 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.

December 30, 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.

Software engineer

Logical Clocks AB are the makers of Hopsworks, a data-intensive AI platform with a Feature Store.

Hopsworks Feature Store For SageMaker For Databricks

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The Feature Stores for ML Newsletter brings highlights, news, events, and insights related to Feature Stores and ML in industry.

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