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Logical Clocks' Blog
Best practices, product updates, research, discussions and more.
CATEGORIES
Company News
Product Updates
ML Best Practices
Industry Discussion
November 19, 2020
HopsFS: 100x Times Faster than AWS S3
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.
Mahmoud Ismail
Software engineer
November 17, 2020
Hopsworks 2.0: The Next Generation Platform for Data-Intensive AI with a Feature Store
Hopsworks is the world's first Enterprise Feature Store along with an advanced end-to-end ML platform.
Theofilos Kakantousis
VP of Product
November 17, 2020
Hopsworks: World’s Only Cloud Native Feature Store, now Available on AWS and Azure.
Hopsworks is now available as a managed platform for Amazon Web Services (AWS) and Microsoft Azure with a comprehensive free tier.
Steffen Grohsschmiedt
Head of Cloud
November 17, 2020
Hopsworks Feature Store API 2.0, a new paradigm.
Hopsworks Feature Store API was rebuilt from the ground based on our extensive experience working with enterprise users requirements.
Fabio Buso
VP Engineering
October 23, 2020
Feature Store for MLOps? Feature reuse means JOIN
Use JOINs for feature reuse to save on infrastructure and the number of feature pipelines needed to maintain models in production.
Jim Dowling
CEO
October 8, 2020
ML Engineer Guide: Feature Store vs Data Warehouse
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.
Jim Dowling
CEO
September 30, 2020
One function is all you need for ML Experiments
Hopsworks supports machine learning experiments to track and distribute ML for free and with a built-in TensorBoard.
Robin Andersson
Software engineer
July 27, 2020
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.
Jim Dowling
CEO
July 16, 2020
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
July 1, 2020
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.
Remco Frijling
Guest Writer
June 26, 2020
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.
Moritz Meister
Software Engineer
June 15, 2020
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.
Jim Dowling
CEO
May 26, 2020
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
CEO
May 18, 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
VP Engineering
April 27, 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
Head of Cloud
April 23, 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
VP Engineering
April 15, 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
VP of Product
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.
Jim Dowling
CEO
Remco Frijling
Guest Writer
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.
Fabio Buso
VP Engineering
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
VP of Product
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.
Jim Dowling
CEO
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.
Moritz Meister
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
March 21, 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
CEO
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...
Jim Dowling
CEO
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.
Jim Dowling
CEO
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.
Jim Dowling
CEO
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.
Jim Dowling
CEO
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.
Kim Hammar
Software engineer
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