Welcome to above the clouds
GCP – How Macy’s enhances the customer experience with Google Cloud services
Editor’s note: Learn from Mohamed Nazeemudeen, Director of software engineering at Macy’s, about Macy’s strategy regarding choosing cloud databases and how Macy’s pricing services leverage Cloud Bigtable under the hood. You can also find Mohamed’s Google Cloud Next ‘21 session on this topic on YouTube. At Macy’s we lead with our aim of fostering memorable […]
GCP – Quantum Metric explores retail big data use cases on BigQuery
Editor’s note: To kick off the new year, we invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that has seen tremendous change. The original version of this blog was published by Quantum Metric. Please enjoy this updated […]
GCP – PyTorch/XLA: Performance debugging on Cloud TPU VM: Part II
This article is part-II of the series on ‘PyTorch/XLA:Performance Debugging on TPU-VM’. In the previousarticle we introduced the basic metrics of performance analysis. We used the client side debugging with the PyTorch/XLA profiler to identify how the .equal() operator used inside the Multihead Attention module implementation caused frequent recompilation of the graph causing the training […]
AWS – AWS Launch Wizard now supports scaling HANA based SAP deployments to meet increased performance requirements
You can now add nodes to SAP systems deployed with AWS Launch Wizard from within the Launch Wizard console post-deployment if your performance needs increase. This allows you to scale the infrastructure supporting SAP applications (S/4HANA, BW/4HANA, and NetWeaver) deployed with Launch Wizard using the same guided, best-practice-aligned deployment process. Read More for the details.
AWS – AWS IoT SiteWise Edge supports new data storage and upload prioritization strategies for intermittent cloud connectivity
AWS IoT SiteWise Edge allows you to define edge data storage strategies to prevent loss of operational data while disconnected (up to 30 days) as well as choosing a data upload strategy to the cloud upon re-connection. Application data uploaded to the cloud is often time critical, where values beyond a certain age may not […]
GCP – 2022 Resolution: Learn Google Cloud, free of charge
Start your 2022 New Year’s resolutions by learning at no cost how to use Google Cloud with the following training opportunities: 30 day access to Google Cloud Skills Boost Register by January 31, 2022 and claim 30 days free access to Google Cloud Skills Boost to complete the Getting Started with Google Cloud learning path. […]
GCP – Where is your Cloud Bigtable cluster spending its CPU?
CPU utilization is a key performance indicator for Cloud Bigtable. Understanding CPU spend is essential for optimizing Bigtable performance and cost. We have significantly improved Bigtable’s observability by allowing you to visualize your Bigtable cluster’s CPU utilization in more detail. We now provide you with the ability to break the utilization down by various dimensions […]
GCP – How Bayer Crop Science uses BigQuery and geobeam to improve soil health
Bayer Crop Science uses Google Cloud to analyze billions of acres of land to better understand the characteristics of the soil that produces our food crops. Bayer’s teams of data scientists are leveraging services from across Google Cloud to load, store, analyze, and visualize geospatial data to develop unique business insights. And because much of […]
GCP – 2022 New Year’s resolution: Learn at no cost how to use Google Cloud
Start your 2022 New Year’s resolutions by learning at no cost how to use Google Cloud with the following training opportunities: 30 day access to Google Cloud Skills Boost Register by January 31, 2022 and claim 30 days free access to Google Cloud Skills Boost to complete the Getting Started with Google Cloud learning path. […]
GCP – Use graphs for smarter AI with Neo4j and Google Cloud Vertex AI
In this blog post, we’re going to show you how to use two technologies together: Google Cloud Vertex AI, an ML development platform, and Neo4j, a graph database. Together these technologies can be used to build and deploy graph-based machine learning models. The code underlying this blog post is available in a notebook here. Why […]