Google Cloud Platform Solutions and Technologies
Solve your business challenges and prepare your team with Google Cloud solutions, products, and services.
Identity and Access management, or IAM, in Google Cloud gives you precise control over the permissions that users have. In this video, will discuss using the Policy Troubleshooter to investigate group membership and resource hierarchy permissions to understand how you can allow users to access and modify resources.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:51 - Example permissions in Cloud Storage1:40 - Using the Policy Troubleshooter2:03 - Understanding the resultsExtra Credit :Policy TroubleshooterTroubleshoot IAM permissions → https://goo.gle/3Q6rJw0Full resource names → https://goo.gle/3CVzwKk
If you are in Chicago this week and are interested in 1) Data, 2) Cloud, or 3) AI, come check out Google Cloud's in-person Data & AI Briefing on Thursday September 8th!What’s happen to this in-person event :Imagine the new possibilities of working together on a unified Data & AI platform to create impactful experiences for your customers. The economy is changing, the volume of data is growing exponentially, and differentiating in this saturated market is becoming more challenging. With cloud, organizations can unlock the potential of hoarded data with the power of Artificial Intelligence (AI) and other technologies that are available at everyone's fingertips. The pace of adoption has been rapid for organizations of all sizes. This immersive, dynamic event will give you all the tools necessary to start transforming your organization with new capabilities. Come join with Rachel Kamienski, AI/ML Specialist,Google Cloud Customer Engineering and others speakers, and let's Create Imp
Fireside chat with ESG industry analyst, Nathan McAfee. Getting the most from big data while reducing costs and complexityData is growing at an explosive rate, both in the velocity of new data creation and the constant addition of new data types and sources. This rapid change in stored data forces organisations to focus on the core fundamentals of data security and cost – too often ignoring the inefficiencies experienced when trying to use the data to enable future revenue. The result: an unrealised value stored in secondary data. Many have adopted on-prem Apache Hadoop to store and process this data, along with Apache Spark. While these solutions provide the base functionality needed, they quickly become limiting. In this video, learn how organizations can increase the value of insights queried in data while lowering costs and complexity by deploying Google Cloud Dataproc. Click on the video below to watch it in detail.
An insight into how Google has removed the generic CLOS Spine capacity challenges present in today's DC fabrics. Google cloud datacentre Jupiter fabrics now use Optical Circuit Switching instead of Spines.click on the video below to watch it in detail:
I installed the free version of the webhosting through GCP free tier with wordpress with the help of youtube. I picked the wordpress version WordPress Certified by Bitnami and Automattic. I want to have a control panel (cpanel/plesk or another one) installed but its not clear to me wich one to choose so this stays free to me. Can someone help me ?
Supply chains are critical across industries, but now more than ever they are under scrutiny. Regardless of where you are in your AI adoption journey, Google Cloud offers AI solutions to meet your supply chain planning needs. Inventory distortion is forcing CPGs & Retailers to rethink demand forecasting. Google Cloud empowers Supply Chain teams to manage better forecast demand and inventory planning needs with advanced AI, powered by Vertex AI. Hear from Google Cloud’s North American Supply Chain & Logistic Director and o9 Global Product Marketing VP on how they are partnering to bring supply chain intelligence to industries. Click on the link below to register for this event.https://cloudonair.withgoogle.com/events/supply-chain-and-planning-webinar-for-vertex-forecasting Agenda Keynote: The Future of Supply Chain Planning & AnalyticsFrom the rapid rise of ecommerce, to labor shortages, to geopolitical and macro economic tensions - CPG & Retail supply chains are be
Discover this new AI/ML video on how to go from prototyping to at-scale production with Nikita Namjoshi. To Prototype to Production where Nikita Namjoshi go from notebook code to a deployed model in the cloud. In this Video, Developer Advocate Nikita Namjoshi shares the foundational concepts you’ll need to build, train, scale, and deploy machine learning models on Google Cloud. Check out this video to learn the benefits of running machine learning in a cloud environment, as well as how to use Notebooks on Google Cloud!Click on the video below to watch it in detail: Chapters:0:00 - Intro0:52 - How does machine learning work in the cloud?1:38 - Set up our environment with Vertex AI2:00 - Create new managed Notebook2:36 - DIfferent Notebook options3:44 - Summary and wrap up Extra Credit:Vertex AI docs → https://goo.gle/3RV7iV7 Vertex AI Workbench codelab → https://goo.gle/3NtnRou 5 steps to go from a notebook to a deployed model blog → https://goo.gle/3cCD0pC Prototype To Production playl
A huge part of the machine learning process is experimentation, luckily there are a few Vertex AI features that can help you with tuning and scaling your ML models. In this video of Prototype to Production, Developer Advocate Nikita Namjoshi takes a look at hyperparameter tuning, distributed training, and experiment tracking. Watch this video to learn how you can get models out of experimentation and into production with Vertex AI.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:49 - Hyperparameter tuning1:28 - Hyperparameter tuning on Vertex AI3:25 - Distributed training5:16 - Configuring worker pools6:00 - Experimentation with TensorBoard7:03 - Vertex AI experiment tracking service7:30 - Wrap up Extra Credit:Hyperparameter tuning on Vertex AI docs → https://goo.gle/3RiRxpT Distributed training on Vertex AI docs → https://goo.gle/3QNgdXz Vertex AI TensorBoard docs → https://goo.gle/3CBiMb4 Vertex AI Experiments docs → https://goo.gle/3pP0U4O Vertex AI Experiment
Google just broke my favorite adage - “if it ain’t broke, don’t fix it”. I still have to find a write up on why Google change or how to solve the problem of losing integration to IFTTT service. Now assistant doesn’t want to turn anything on/off using my diy HA.
The API proxy deployment process in Apigee X has two main prerequisites; networking configuration and routing configuration. If these configurations are invalid, the API proxy endpoints may not be accessible. In this video you will learn Apigee X networking architecture and the process of verifying networking configuration in an Apigee X organization.Click on the video below to watch it in detail: Also you can check the video link below to verify routing configuration in Apigee X
Are your API requests not reaching the API Proxies in Apigee X? Are you observing 503 errors for your API Proxies due to incorrect routing configuration? Then check out this video to learn about the routing data model, how it is applied in Apigee X instances, how to verify the routing configuration and ensure the API requests are routed to the correct API Proxies in an Apigee X organization.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:34 - What is the routing data model?1:48 - Example single-region solution architecture2:25 - Example multi-region solution architecture3:11 - How to verify environment attachments of instances?3:53 - How to verify hostname configuration in the environment group?4:18 - How to verify API proxy configuration?4:46 - How to verify the API request is correctly routed to the API proxy? Also, you can check the video link below to verfy networking in Apigee X
In this demo video, You can walk through a web application that Google cloud built using the Document AI for Identity API . This video will show you how to extract and normalize data from dummy licenses and also run our fraud detector on sample data. Click on the video below to watch it in detail:
Google’s Data Cloud helps customers innovate faster with a unified and intelligent data platform. Watch this demo to see how a data analyst at Cymbal Direct - our fictitious fashion retailer - uses Google’s Data Cloud, starting with BigQuery, to uncover insights around fleet and fuel inefficiencies. This end-to-end demo showcases how Google’s Data Cloud works across data types, sizes, and clouds to get fast insights, models data with built-in machine learning, and allows for easy sharing with business stakeholders.Click on the video below to watch it in detail:
Batch jobs typically require complicated setup, ongoing management, and high costs. Introducing Google Batch from Google Cloud, a fully managed job scheduler to help you run thousands of batch jobs with a single command. Watch along with Debi Cabrera, Developer Advocate, Google Cloud and learn how to optimize resources to match your batch job’s needs while maintaining flexibility regarding cost and resource availability.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:28 - Batch jobs today1:20 - What is Batch from Google Cloud?3:36 - What is a fully-managed batch job scheduler?4:19 - How to define and run a batch job on Google Cloud9:09 - Wrap up
L’Oreal is a global company with a presence in 150 countries worldwide. Between managing all of its brands and requirements for different countries, L’Oreal looks to data to make insightful business decisions. How does L’Oreal unify its data across all its systems and databases? How does L’Oreal make the data accessible to thousands of employees? In this video, Antoine Castex, Enterprise Architect at L’Oreal, discusses with Martin Omander how L’Oreal built a serverless, multi-cloud warehouse based on Google Cloud.Click on the video below to learn how Antoine Castex designed a complex data warehouse solution using Google Cloud. Chapters:0:00 - Intro0:23 - Why does L’Oreal need a new data warehouse?0:51 - Who is the L’Oreal group?1:35 - Which systems does L’Oreal use?2:14 - How does L’Oreal manage complexity?3:59 - What is ELT?4:57 - Who are L’Oreal’s data consumers?5:41 - How L’Oreal built the data warehouse8:51 - L’Oreal’s future plans9:10 - Wrap upExtra Credit:Google Cloud Workflows →
Google Cloud’s Dataflow recently announced the General Availability support for Apache Beam's generic machine learning prediction and inference transform, RunInference. In this article, Reza Rokni, Senior Staff Developer Advocate Dataflow show us to take a deeper dive on the transform, including: Showing the RunInference transform used with a simple model as an example, in both batch and streaming mode. Using the transform with multiple models in an ensemble. Providing an end-to-end pipeline example that makes use of an open source model from Torchvision. He also discussed, about Apache Beam developers who wanted to make use of a machine learning model locally, in a production pipeline, had to hand-code the call to the model within a user defined function (DoFn), taking on the technical debt for layers of boilerplate code. Let's have a look at what would have been needed: Load the model from a common location using the framework's load method. Ensure that the model is shared am
Every tech and startup customers ask to google cloud about how to build apps faster, smarter, and cheaper. For that reason, Google Cloud Product Manager Rachel Tsao explores how to save on compute costs with modern container platforms. He advised 5 key points to reduce the container cost for startup and tech customers that might help you. Identify opportunities to reduce cluster administration Consider serverless to maximize developer productivity Save with committed use discounts Leverage cost management features Match workload needs to pricing modelsClick on the link below to read more detail:https://cloud.google.com/blog/topics/startups/5-ways-to-reduce-costs-with-containers-on-google-cloud
Hear from Google experts and Google cloud partners on the latest insights in AI/ML. Join google cloud for a full day of use cases, demos, labs, and discussion on all things AI/ML.Date and Time: 20 September 2022 at 14:00 - 20 September 2022 at 23:00 BSTEvent Type: In-personLocation: Google Chicago Office320 N Morgan St Suite 600Chicago60607USClick on the button or link below to register for this event.https://rsvp.withgoogle.com/events/ai-ml-chicago-dayAgendaTime Topic 08.00 AM Breakfast & Networking 08.45 AM Kickoff & Welcome 09.00 AM AI/ML 2022 Industry Trends and Business Use Cases 09:30 AM Overview of GCP AI Portfolio10.00 AM Vertex AI Overview and Use Cases10.45 AM Break11.00 AM Quantiphi Intro & Overview11.15 AM Doc AI Overview and Use Cases12.00 PM Lunch & Networking12.45 PM Visual Inspection AI Overview and DemoBreakout1 1.30 PM MLOps Hands on Lab and AutoML OverviewBreakout 2 1:30 PM Anomaly detection 2:15 PM Conversational AI 03.00 P
Do you currently know each location you might store sensitive data? How about the data you collect from customers and partners? If not, you’re not alone. In this video, Scott Ellis, Product Manager, Google Cloud will cover how Google can help you gain understanding and visibility into what data you are storing, and where it is located - regardless if it’s unstructured data in BigQuery or scanned images in storage. This allows you to take appropriate measures to protect your data, and build a comprehensive data security strategy that adapts as quickly as your data changes. To view it in detail, click on the video below: https://cloudonair.withgoogle.com/events/emea-security-talks/watch?talk=talk7