Welcome to the feature store newsletter brought to you by Logical Clocks, where we in a monthly cadence highlight the latest news, events, and insights as we help companies become successful in their machine learning transformation journey and empower businesses to be applied-AI model-driven companies.
Cost-Benefit Analysis of a Feature Store for ML
When you invest money in machine learning (ML), you typically start by investing in people. You hire data scientists, data engineers, and ML engineers to transform your data into insights that can help you both reduce costs and increase revenue. However, if you do not manage the ML assets you create (the feature engineering jobs, the feature data, the models, and the CI/CD pipelines), the cost of each ML project will be roughly constant - every new project will start over from scratch and your ML-readiness will grow slower that the leading companies in your field. That is because the leading ML companies have all invested in building a data platform for ML (aka a feature store).
This month we highlight the cost reductions and productivity improvement metrics of feature stores. Click at the image to read our cost-benefit analysis of feature stores and to have access to our Feature Store ROI Calculator which will help you estimate the return on investment that your business would have if a feature store were implemented into your ML pipeline.
Accelerating Machine Learning as a Service with Automated Feature Engineering
Cognizant wrote a white paper on feature stores for ML with insights into why enterprises need a fully functional feature store in their ML maturity journey.
If Mom Taught Me Anything … It’s That You Need a Feature Store
Alex Vinnik at Sailpoint wrote about how they developed a feature store to support their Identity platform.
The Importance of Having a Feature Store
Ido Zehori from BigaBid wrote up on their experiences with building a feature store and the value it has added to their organization.
5 Minimum Requirements of an Operational Feature Store
Ben Epstein, Sergio Ferragut, and Monte Zweben listed out some of the minimal requirements of Feature Stores.
How to Secure your Data with Hopsworks
Antonios Kouzoupis from Logical Clocks wrote on how to integrate with third-party security standards and take advantage of Hopsworks’ project-based multi-tenancy model.
Feature Stores in the News
Some Things Uber Learned from Running Machine Learning at Scale
Published on KDnuggets - Uber machine learning runtime Michelangelo has been in operation for a few years. What has the Uber team learned?
Logical Clocks Joins European Initiative to Bring AI to 5G Networks with Hopsworks Feature Store
Published on Datanami - Logical Clocks announced it is developing the first enterprise Feature Store for Edge Computing to bring artificial intelligence to 5G networks in Europe.
Tecton brings DevOps to machine learning
Published on ZD Net - Tecton aims to automate much of the process for feature development with the goal of making data scientists more self-reliant.
Upcoming Webinars & Events
OpML20 Conference - Time Travel and Provenance for Machine Learning Pipelines
This presentation discusses an approach based on replacing traditional intrusive hooks in application code (to capture ML pipeline events) with standardized change-data-capture support in the systems involved in ML pipelines: the distributed file system, feature store, resource manager, and applications themselves.
Webinar - Solve Fraud Challenges with Graph Network and Deep Learning in Hopsworks
Learn how to use deep learning (both supervised & unsupervised learning) and graph embeddings as potential technologies to increase anomaly detection rates and reduce costs associated with fraud and anti-money laundering, using our Hopsworks data platform.
Tech Lead for Feature Lifecycle Tools
Twitter is hiring a Tech Lead to join their ML Feature Management (MLFM) team in the USA.
Sr Software Engineer - ML Feature Store
Cortex is hiring a new addition to its teams in the USA to efficiently leverage ML.
Data Scientist - ML frameworks and Infra
Shopee is hiring a data scientist in Singapore to work in machine learning and software engineering.
PicPay is hiring a front-end developer in Brazil to create a complete and optimized Feature Store.
Sr Manager, Engineering (Scale & Transport)
Quantcast is hiring an engineering manager to scale their feature store.
Machine Learning Engineer
STATS is hiring an ML Engineer to be responsible for de-bias, ethics, security and compliance aspects of ML pipelines.
If you are just starting your career in data science, this blog might for interesting to you.
Latest Videos & Podcasts
Video - Manage your own Feature Store on Databricks
Jim Dowling from Logical Clocks walks through a demo showing how to engineer your features on Databricks and publish them to Hopsworks Feature Store.
Video - Lessons Learned Rebuilding a Large Scale Real-time Feature Store
Kristi Tsukida from Quantcast shares his insights learned from using Aerospike to replace large scale real-time feature stores.
Video - Feature Stores: An essential part of the ML stack to build great data
Kevin Stumpf from Tecton shares his insights on the challenges of getting ML features to production.
Podcast - Accelerate your Machine Learning with a Feature Store
Simba Khadder, CEO at StreamSQL, discusses his work at StreamSQL building a feature store to make creation, discovery, and monitoring of features fast and easy to manage.