When it comes to software delivery and deployments, details matter. Earlier this year, we added parallel deployments to Cloud Deploy. Since launching that feature, we’ve received feedback to support differentiated release deployments across individual child targets. To enable this, we are pleased to introduce deploy parameters to Cloud Deploy in Preview, allowing you to configure […]

This post is the first in a short series of blog posts about Feast on Google Cloud. In this first blog post, we describe the benefits of using Feast, a popular open source ML feature store, on Google Cloud. In our second blog post, we’ll provide a simple, introductory tutorial for building a product recommendation […]

Data is the lifeblood of any machine learning application. To make more accurate predictions, ML models need access to high-quality data signals, commonly referred to as ML features. These ML features are usually characterized by refined data with high signal value that incorporate all the latest information available to an organization. Tecton on Google Cloud […]

Google Cloud provides a wide range of services for running data and analytics workloads, which can mean sifting through a lot of information when choosing the right tools for your specific use cases. Each workload requires a unique set of services, from data ingestion and processing to storage, governance, and orchestration. To simplify the decision-making […]

Data practitioners know that relational databases are not designed for analytical queries. Data-driven organizations that connect their relational database infrastructure to their data warehouse get the best of both worlds: a production database unhassled by a barrage of analytical queries, and a data warehouse that is free to mine for insights without the fear of […]

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