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Hello all, I did develop a script with Google App Script. Now I would like to offer the opportunity to all my workspace user to be able to install it with a click. So I would like to have my App been integrated in the our Google Workspace PRIVAT Market place. The script I developed is an extention for Google Sheet. I watched many video about it and even bought an traning but I could find anything to show step by step from the beginning to the end how to deploy my app. Thank you very much for your help. Looking forward to read from you.
Customer’s need for a connected decisions(connected planning or bridging planning with execution) leads them to look for a Digital Twin, Control Tower or a Command Center. Most often business think there are standard software that addresses the need. In my two decade experience(since I started my career at Cisco). most softwares don’t even meet 70% of the need, hence customer end up building on their own. This leads to my point, that connecting data is essential for you to take the journey ahead and for which you need to start with a data foundation platform and then evolve. Traditionally, a supply chain control tower is defined as a connected, personalized dashboard of data, key business metrics, and events from across the supply chain. A control tower for supply chain allows businesses to better understand, prioritize, and resolve critical issues in real time. A smarter control tower should provide end-to-end visibility across the supply chain, especially in the event of unanticipate
I am currently evaluating different Google Cloud products in trying to set up an API for developers to use in a mobile app. How should I evaluate thinking between:A single Cloud Function using Express.js to create multiple routes in an API (accessed via API Gateway), vs. A single Cloud Function per API endpoint (accessed via API Gateway)
Run your AI/ML, video transcoding, and other GPU workloads on Google's fully managed Kubernetes platform! NVIDIA T4 and A100 GPUs are now available on GKE Autopilot.The great thing about running GPU workloads on Autopilot is that all you need to do is specify your GPU requirements in your Pod configuration, and Google Cloud take care of the rest. No need to install drivers separately, or worry about non-GPU pods running on your valuable GPU nodes, because Autopilot takes care of GPU configuration and Pod placement automatically. Click on the link below to read it more detail. https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot
Would you like to learn how to troubleshoot autoscaling issues in Dataflow ? In particular, are you observing that your streaming jobs are not scaling down? Would you like to know more about the autotuning features in Cloud Dataflow? Check out this video below to learn about the Autotuning features Horizontal autoscaling, Vertical autoscaling, and Dynamic Work Rebalancing. Watch this video to learn about the possible causes for streaming jobs not scaling down and how to resolve the same. It also covers the streaming engine and its benefits.Click on video below to watch it in detail: Chapters:0:00 - Intro0:16 - Autotuning features1:44 - Problem: Streaming job not scaling down1:59 - Validation of the criteria for autoscaling2:20 - Limitations of streaming appliance2:34 - Example of limitations imposed by streaming appliance3:05 - Cause for streaming job not scaling down3:20 - Solution for the problem3:33 - Benefits of using the streaming engine3:57 - Documentation and conclusion Extra cr
Learn how to troubleshoot issues with Anthos clusters running on VMWare using the gkectl command line tool. Additionally, learn how to generate snapshots you can send to Google Cloud support if further analysis is required.Click on video below to watch it in detail: Chapters0:00 Intro0:13 What is gkectl?0:53 How to diagnose Anthos admin cluster using gkectl ?1:36 How to fix Admin cluster issues?2:33 How to diagnose Anthos user cluster using gkectl?2:58 What is a snapshot?3:18 Snapshot creation process3:38 VMWare snapshot creation prerequisites4:00How to create a snapshot for admin cluster using gkectl Diagnosing cluster issues on VMWare → https://goo.gle/Diagnose_Anthos_On_Vm...
[Customer story]How Volkswagen and Google Cloud are using machine learning to design more energy-efficient cars
A vehicle’s drag coefficient is an important factor of energy efficiency, but estimating drag coefficient is expensive and time-consuming. What a drag... ...until Google cloud worked with Volkswagen of America, Inc to use ML to get fast and inexpensive estimates of the drag coefficientClick on the link below to read it more detail:https://cloud.google.com/blog/products/ai-machine-learning/volkswagen-uses-google-cloud-ai-for-more-efficient-cars
This week on the GCP Podcast, hosts Anu Srivastava and Nikita Namjoshi are joined by Ivan Nardini and Karthik Ramachandran to learn how Vertex AI Experiments in conjunction with other Google Cloud tools can improve ML models Click on the link below to read it more detail and watch the expriement demo.https://www.gcppodcast.com/post/episode-320-vertex-ai-experiments-with-ivan-nardini-and-karthik-ramachandran/ https://podtail.com/en/podcast/google-cloud-platform-podcast/vertex-ai-experiments-with-ivan-nardini-and-karthi/
Since 2018, the Flood Forecasting team at Google Research has been working to apply advanced machine learning methods to a broad range of data sets, such as satellite imagery, river data, and weather data. These data sets help to generate high-quality flood forecasts that predict when rivers will overflow and the exact locations where flooding will take place.The system currently covers an area populated by more than 370 million people across several countries in South East Asia and Latin America. In 2021 the Flood Forecasting system reached 23 million people with more than 115 million notifications based on user location and flood maps. Flood Forecasting and alerting plans to expand globally in the near future to ensure everyone around the world can benefit from these early warnings.If you have five minutes and want to know how this works then watch this video: https://www.youtube.com/watch?v=Mz0ikfuE_z0 Background to this post:In 2015, the UN set the Sustainable Development Goals, am
Martin and Veer moved a to-do app from a virtual machine to Cloud Run. However, the database is still running on the virtual machine. Watch along and learn how to move a database from a virtual machine to a low-ops, serverless database like Firestore.Click on the video below to watch it in detail: Chapters:0:00 - Intro2:34 - Export from MongoDB3:23 - Transform the data4:24 - Configure Firestore5:00 - Import the data into Firestore7:35 - Update the application9:14 - Deploy and test the application9:45 - Wrap up Extra Credit:How to deploy an existing web app on Cloud Run → https://goo.gle/3S5mNZz Migrating Collections from MongoDB to Cloud Firestore → https://goo.gle/3xie4vq MongoDB version → https://goo.gle/3U5Rb7Y Finished code → https://goo.gle/3xKAfKQ
You’ve spent tons of time creating your virtual machine, and now you’re being asked to install something that may cause issues and make your life harder. Have you considered running it in an ephemeral Cloud Run instance instead? In this video, Brian and Katie discuss different patterns for using Compute Engine VMs and Cloud Run together! Watch along and learn how to combine them or decide between them.Click on video below to watch it in detail: Chapters:0:00 - Intro0:41 - What is Cloud Run?2:18 - How does Cloud Run work with VMs?3:45 - What are containers?5:15 - What is Migrate to Containers?6:33 - What workloads are a good fit for Cloud Run?7:56 - When should Cloud Run be used with VMs?11:14 - How to get started with Cloud Run12:15 - Wrap upExtra Credit:Richard Seroter's Demo → https://goo.gle/3SjSD5b Getting Started With Cloud Run, with language microservices: → https://goo.gle/3Um1kxr Getting Started With Cloud Functions → https://goo.gle/3Ls94da Google Cloud Buildpacks: → https://
First trying ML in the cloud, many practitioners will start with fully managed ML platforms like Google Cloud’s Vertex AI. Fully-managed platforms abstract out many complexities to simplify the end-to-end workflow. However, like with most decisions, there are tradeoffs. Organizations may choose to build their own custom, self-managed ML platform for various reasons such as control and flexibility. Building your own platform gives you more control over your resources. You can implement unique resource utilization constraints, access permissions, and infrastructure strategies that fit your organization’s specific needs. You also get more flexibility over tools and frameworks. Since the system is completely open, you can integrate any ML tools that you already are using. And lastly, these benefits help avoid vendor lock-in because cloud-native platforms are by definition portable across cloud providers.Richard Liu, Senior Software Engineer, Google Kubernetes Engine and Winston Chiang, Pro
Are you unsure when to use service accounts? Not sure how to grant applications access to Google Cloud Resources? When to use the OAuth consent flow? Then check out this video and learn the best practices for using and managing service accounts. This video also cover how to choose the right authentication method when using a service account based on your use case.Click on the video below to watch it in detail:Chapters:0:00 - Intro0:07 - What are service accounts?0:23 - When to use service accounts?1:23 - OAuth consent flow1:49 - Authentication methods2:53 - Best practices for managing service accounts Best practices for working with service accounts → https://goo.gle/3C11hQO
When you think of a service mesh, you probably think of “sidecar containers running with each pod”. The Istio team has come up with a new approach, introduced recently as an experimental preview. Google Cloud software engineers Justin Pettit and Ethan Jackson join Craig to explore ambient mesh.Click on the following link to visit the Kubernetes Podcast:https://kubernetespodcast.com/episode/189-ambient-mesh/
In 2015, the UN set the Sustainable Development Goals, ambitious targets for a better and more sustainable future by 2030. AI can reduce the time and resources needed to progress against some of the world's toughest challenges. Google.org is committing $25M to fund solutions accelerating progress towards the Global Goals. In this post, I will introduce Rainforest Connection, one of the Google.org grantees that uses AI to accomplish its mission.Rainforests naturally absorb carbon dioxide from the atmosphere, making them a vital component in fighting climate change — but they are under profound threat from illegal logging - which accounts for more than half of logging in rainforests. Rainforest Connection develops acoustic monitoring technology that uses AI to protect vulnerable ecosystems by detecting threats like illegal logging in real time. The solar-powered acoustic devices used for threat detection, called RFCx Guardians, transmit audio to the cloud. Then, AI analyzes and classifie
We know how difficult it is to solve problems like fraud detection, ad targeting, and recommendation engines, which require near real-time predictions. The accuracy of these predictions is heavily reliant on access to the most recent data, with delays of just a few seconds making all the difference.However, it is difficult to set up the infrastructure required to support high-throughput updates and low-latency data retrieval.Erwin Huizenga and Kaz Sato, Developer Advocate, Google Cloud wrote an article based on the latest Vertex AI updates. Vertex AI Matching Engine and Feature Store will support real-time Streaming Ingestion as Preview features. With Streaming Ingestion for Matching Engine, a fully managed vector database for vector similarity search, items in an index are updated continuously and reflected in similarity search results immediately. With Streaming Ingestion for Feature Store, you can retrieve the latest feature values with low latency for highly accurate predictions, a
Google Cloud always updates its applications and infrastructure for its customers' needs. Recently Google Cloud updates on Google Cloud Spanner and Datastream. Here are a couple of updates on Google Cloud Spanner and Datastream. To read it in detail click on the links below.Datastream for BigQuery is in Public Preview:Datastream for BigQuery leverages the same serverless architecture and Change Data Capture (CDC) capabilities. Now with a direct BigQuery destination, customers can easily and seamlessly replicate data from their source databases into BigQuery, saving time and resources.There is also the public preview of Datastream support for PostgreSQL sources https://lnkd.in/gWekJ2qhFor Cloud Spanner there is General Availability launch of the open-source Google Cloud Spanner driver for database/sql in Go! (https://lnkd.in/gYmmbNWa)https://rb.gy/ggw9n5and in public preview fine-grained access control for Cloud Spanner which provides table- and column-level protection for Spanner dat
Are you a startup striving for growth and innovation? Are you looking to innovate, revamp and make the best use of your cloud infrastructure? Or are you simply looking to unbox, learn & grow your business with a leading cloud services provider?We at Google Cloud are here to help you learn & understand GCP cloud services and use them to improve your technical landscape. Our focus is to empower growing startups like yours with the right training, tools, technologies, interactive product workshops and support.We are thrilled to announce 60 minutes of monthly interactive sessions on GCP Products & Services conducted by Google Customer Engineers.These detailed sessions & workshops have been especially curated for your organization’s needs and cater to specific use cases that you are working on followed by Q&A discussions with Google Cloud experts.Our customer engineers will be available for 1:1 engagements with startups wanting to discuss architectures, migrations, solut
Intel has provided passes to C2C to invite you to be their guest at their developer conference, Intel Innovation, September 27-28 at the San Jose McEnery Convention Center. Intel Innovation 2022 is focused on driving growth and amplification of developer skills at all levels of the stack.The first 100 to register at intel.com/innovation can use the special Google Developer free pass code GMB at checkout.
I know that global variables in C sometimes have the extern keyword. What is an externcorewalking variable? What is the declaration like? What is its scope?This is related to sharing variables across source files, but how does that work precisely? Where do I use extern?
Where should your application store data?Of course, the choice depends on the use case.This post covers the different storage options available within Google Cloud across three storage types: object storage, block storage, and file storage. It also covers the use cases that are best suited for each storage option.
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