Google Cloud Platform Solutions and Technologies
Solve your business challenges and prepare your team with Google Cloud solutions, products, and services.
BigLake in a minute [Video]
Does your organization store data in multiple places? Are you looking for ways to break down data silos and enable analytics? Are you looking to build a multi-cloud data lake on open file formats?Introducing BigLake, a storage engine that allows organizations to unify data warehouses and lakes. Watch along and learn how to perform uniform fine-grained access control and accelerate query performance across multi-cloud storage and open formats all while maintaining a single copy of your data.Take two minute to watch the following video about Big Lake
Build a restaurant edge solution with Google Cloud | demo
Wondering how restaurants and retail establishments use quality control or estimate demand? In this instalment of Build With Google Cloud, Priyanka Vergadia demonstrates how to create an edge solution for a hypothetical hamburger chain that wants to deploy and use uniform technology across all of its locations. Watch this demonstration to see how to update a restaurant and predict demand with Google Cloud! Chapters:0:00 - Intro1:16 - Edge Patterns - IoT1:37 - Rundown of scenarios4:13 - Anthos for Edge5:04 - Demo - Demand Forecasting8:50 - Demo - Dining Room Cleanliness10:39 - Wrap up
Training custom models on Vertex AI
A managed ML training service can help you automate experimentation at scale or retain models for a production application. In this following video Prototype to Production, Developer Advocate, Nikita Namjoshi, walks through the steps required to train custom models on Vertex AI. Watch along and learn about the benefits of a managed training service that helps keep your results fresh. Chapters:0:00 - Intro0:22 - Why do I need a machine learning training service?1:26 - What are containers?2:19 - Update custom training code3:23 - Cloud storage for machine learning4:50 - Containerizing code for machine learning5:39 - Dockerfile syntax6:42 - How to store container images in Google Cloud7:21 - How to launch a training job on Vertex AI8:12 - Wrap up
IOT Intelligent Products Essentials Reference Architecture in Google Cloud
In this discussion, I will focus on the reference architecture for technical decision makers who want to connect devices and build Cloud IoT apps on Google Cloud using Intelligent Products Essentials.At this moment, Manufacturers want to continuously improve their products by adding intelligent capabilities that delight customers and monetize new features. In this article, I will describe how google helps architects tasked with designing intelligent product systems on Google Cloud that are scalable, reliable, secure, and cost-effective.Google Cloud provides capabilities for connecting, ingesting, storing, analyzing, and retrieving data from products to build in artificial intelligence and machine learning capabilities, such as a personalized product ownership app or a digital twin simulation. Here I will present an overview of architecture, its high-level components, integration topics, and general design considerations. The architecture includes the following:The architecture includes
Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build
MLOps, like DevOps, is used to enhance the quality and minimise the time to market of machine learning engineering. It may improve team cooperation, increase the reliability and scalability of ML systems, and minimise development cycle times.It can keep the ML system from failing due to a failure to respond to environmental changes. Here is a detailed look at the finest cloud documentation-based MLOPs implementational architecture.This article demonstrates, using real-world examples, how you can practise MLOps and acquire ideas for constructing valuable solution infrastructure.this document covers the following topics:Understanding CI/CD and automation in ML. Designing an integrated ML pipeline with TFX. Orchestrating and automating the ML pipeline using Kubeflow Pipelines. Setting up a CI/CD system for the ML pipeline using Cloud Build. Please click on the following link to read more details:https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-
Google podcast: Vertex Explainable AI with Irina Sigler and Ivan Nardini
Max Saltonstall and new presenter Anu Srivastava are in the studio to discuss Vertex Explainable AI with guests Irina Sigler and Ivan Nardini. Vertex Explainable AI arose from the desire for developers to better understand how their algorithms select classifications.This categorization understanding is critical for two reasons: trusting the operation of models for business decision making and faster debugging. Also, They dicuss in GCP Podcast, why explainable models are so crucial and how Vertex Explainable AI may assist.To hear and read the transcript, click on the following podcast link:https://www.gcppodcast.com/post/episode-314-vertex-explainable-ai-with-irina-sigler-and-ivan-nardini/
Careers in cloud: Getting started on Google Cloud
If you're like many people new to Google Cloud, you've worked with other public clouds in the past.Join this event on August 24th for a friendly, factual, non-competitive exploration about what's different in Google Cloud - intended to help people more familiar with AWS and Azure overcome initial hurdles and accelerate their cloud journey.You’ll also have an opportunity to ask questions and receive answers live.
build a AI adhar chatbot
hello everyone , i am planning to build a project in which i want build a adhar type chat bot in my college project .so i need help of a person who has good knowledge of AI/ML .i ask everyone that for buliding adhar type chatbot system which technology should i learn so that i can make better version of adhar chatbot and gives solutions of many problelms which are encountered by many citizens
Introducing easier de-identification of Cloud Storage data
Data de-identification, a type of dynamic data masking, refers to severing the connection between the data and the person with whom it was first linked. In essence, this calls for the removal or transformation of personal identification.Data has evolved into a vital and important reservoir of value for businesses. As a result, businesses must safeguard and manage data in a secure and effective manner. Google provides Cloud Data Loss Prevention (CDLP). You may examine, check out, and de-identify your data with the aid of this completely managed service. Using CDLP, you may examine millions of pieces of data and determine which ones are sensitive and have to be de-identified or encrypted.Additionally, you may set it to automatically allow it to disguise your private information (this operation is known as de-identifying). More than a hundred infoTypes are available in CDLP. A type called an infoType is used to represent sensitive data, such as a date, a name, an email address, or a phon
How SkyTruth use Google cloud and ML help to scan the world’s oceans to detect the signs of oil slicks?
In the ocean, how many oil slicks are there? They have no idea where they are or how they got there.You want to know about how a nonprofit environmental watchdog Google Maps, Machine Learning, and Cloud Computing are being used by SkyTruth to analyse radar satellite photos in order to look for indicators of oil slicks.The conservation technology organisation SkyTruth is redefining how they track environmental impacts using Google Maps Platform, machine learning, and cloud computing, according to Mitchelle De Leon, Director of Impact and Strategic Partnerships at SkyTruth.Additionally, he provides a detailed look at SkyTruth's Cerulean, a platform that use artificial intelligence to examine hundreds of radar satellite photos each day in search of indicators of oil slicks.Cerulean will detect oil slicks from ships, offshore oil rigs, and other sources as SkyTruth intensifies the automation process. This will enable the creation of a worldwide map of oil pollution and the identification o
Google Cloud Security Talks 2022
Join the event “Show-and-Tell” Edition of Google Cloud Security Talks.This Google Security Talks will not just share what you need to know about google portfolio of products, it will show you how to use them. Explore Google cloud latest security products and innovations in a unique and refreshing way: by watching google cloud security experts put the solutions they helped build to the test via hands-on, fast-paced and highly visual demos. Each best-practice session will highlight top use cases for Google’s key security solutions and demonstrate step-by-step instructions for how you can apply these exact same scenarios in your own environment. Don’t just see products in action. Learn how you and your team can extract real value. Join the Google Cloud Security Talks to learn more! *To best accommodate varying schedules, Google Cloud security experts are offering two Security Talks sessions per topic throughout the day. Register for your preferred session time below.* Plus, enjoy a spec
Google Colab: Issue Updating Data Links In Excel After Python Dataframe Export
SituationI'm working on a data project integrating python in Google Colab and Excel 365 on Win 8.1. My python code collects new data updates on a regimented schedule and then exports/writes (e.g. overwrites, not appends the data) like to a report on an Excel spreadsheet. I have no issue getting this to work going to a standalone spreadsheet.I know I could potentially do all this in Python and not use Excel at all, but I prefer not to reinvent the wheel and not spend hours hardcoding all the formulas and links already existing in Excel. GoalMy goal is to: Use new data from my Colab export to populate/overwrite a data table on Sheet A in an existing Excel workbook. Then I have a separate Sheet B in the same Excel workbook performing calculations via pre-existing links connecting to the original data table on Sheet A. I then want the links to auto update each time my python export updates the data table on the first sheet. ProblemThe issues I am running into are that if I use the df.to_
Using the Kubernetes Engine to manage ML deployments
Without interruption, DevOps is implemented in the Kubernetes engine throughout the creation of AI/ML applications. To manage application deployment situations like "Continuous Deployment," "Blue-Green Deployments," "Canary Deployments," and more, Dev Ops procedures will frequently require numerous deployments. In this article, I'll go over some fundamental container scaling and management techniques so you can complete these frequent tasks when using several heterogeneous deployments.What I will disscus: Kubernet engine kubectl tool command Create and manage deployment yaml files How can, update, and scale deployments Updating deployments and deployment stylesI hope you have some basic knowledage about Kubernet engine and DevOps theory. About Heterogeneous deployments Heterogeneous deployments usually involve the connection of two or more distinct infrastructure environments or regions to respond to a specific technical or operational need. Heterogeneous deployments are known as "hybr
Getting your first Google Cloud job as a Woman In Tech
The Google Cloud Career, where google developer advocate show how you can…well, crack your cloud career! In this video, Developer Advocate Stephanie Wong shares how women can break into and succeed in tech. In an industry where women have historically been underrepresented, the cloud domain is widening the landscape for women to contribute and flourish.Click on the following video or link to get more details: Chapters:0:00 - Intro1:24 - Don’t let myths stall your career2:07 - Focus on a specialization3:15 - Build a support system4:05 - Find a good mentor5:21 - Don’t let self doubt get in the way6:20 - Stay authentic6:57 - Wrap up Cracking The Google Cloud Career playlist → https://goo.gle/CrackingCloudCareersSubscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Ask Me Anything: Preparing for peak traffic events
Whether it’s a product launch, new store opening, limited-time sale, or holiday, peak traffic events are critical for your business. Don’t just hope that they’ll run smoothly! Join this event for Ask Anything session on August 25th to learn how to successfully plan for peak traffic and launch events to avoid any disruptions for your customers and your business.The experts will cover the three key components in preparing for a successful event:Preparation: Activities that can help you prepare for your event include an architecture review, load testing, quota limits and/or capacity planning - just to name a few. Execution: As your event begins you’ll need to closely monitor and react accordingly. Analysis: After your event is completed, analyze what went well, what didn’t, and how to improve for future events.As always, you’ll also have an opportunity to ask the experts questions and receive answers live. With this series of Ask Me Anything events, it's gogole cloud goal to provide a tru
Dataflow Observability, Monitoring, and Troubleshooting
Join on August 17th at 9AM PT for a live session with Google Cloud experts on Dataflow observability, monitoring, and troubleshooting.Google Cloud expert will provide an overview of all the observability experiences in Dataflow and discuss more on the new features. They will also look at the common symptoms or issues reported by customers for streaming pipelines and discuss how to use the new observability features and tools to troubleshoot those.To participate in the live session on August 17 at 9 a.m. Pacific Time, click on the video link below. Video chapter:03:02 Evolution of data and the value gap06:21 Why Dataflow?08:44 What is Dataflow?10:25 What is observability? Core Dataflow observability features:11:48 Job visualizers14:38 Job metrics21:06 Cloud Error Reporting integration22:19 Cloud Profiler integration23:33 Dataflow insights (recommendations)25:21 Datadog integration26:54 Where to start?29:23 Live demo Troubleshooting common scenarios48:31 Job slows down with increase in
Demystifying Machine Learning with Google Cloud
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
Advanced Medical Entity Extraction with AI in Google Cloud.
Recently Google cloud worked with Apollo 24|7, the largest multi-channel digital healthcare platform in India, to build the key blocks of their CDSS solution. It is a crucial piece of healthcare technology, the Clinical Decision Support System (CDSS), that analyses data to assist healthcare practitioners in making decisions regarding patient care.Google cloud AI helped them to parse the discharge summaries and prescriptions to extract the medical entities. These entities can then be used to build a recommendation engine that would help doctors with the “Next Best Action” recommendation for medicines, lab tests, etc.Vertex AI, a Python package comprising spaCy models for analysing biomedical, scientific, or clinical literature, is used by Google cloud to conduct research based on ScispaCy.To learn more about Google Cloud and the Apollo 24|7 Building Clinical Decision Support System (CDSS), click the link below:https://cloud.google.com/blog/products/ai-machine-learning/apollo-24-7s-cdss-
Bundesliga Data Shoot Out - New Kaggle competion
Potential to win $12000. In this competition, you'll detect football (soccer) passes—including throw-ins and crosses—and challenges in original Bundesliga matches. You'll develop a computer vision model that can automatically classify these events in long video recordings.Your work will help scale the data collection process. Automatic event annotation could enable event data from currently unexplored competitions, like youth or semi-professional leagues or even training sessions. https://www.kaggle.com/competitions/dfl-bundesliga-data-shootout Good luck
Google Cloud Deploy enhancements now available!
Have you seen the blog post just published today about Google Cloud Deploy and the enhancements that are now available? If you haven’t, go check it out! Thanks to the work of the Google Cloud Deploy team, continuous software deployment to GKE is getting easier to manage.Google Cloud Deploy was only released to general availability in January 2022, and the team is actively enhancing its capabilities. In this update, Google Cloud Deploy has added auto generation of a Skaffold configuration for single manifest applications. This is a nice jump start for teams who aren’t familiar with Skaffold.Delivery management improvements include the ability to pause a pipeline temporarily, and to abandon a release. Release Inspector is a shiny new difference comparison tool that enables users to more easily review application manifests.From an enterprise perspective, this update allows you to deploy Google Cloud Deploy delivery pipelines and target resources using Google Cloud Platform’s Terraform pro
Data transformation in BigQuery
Hello All, I need to a build transformation to load a target table from 80+ source tables (all source tables are staged into BigQuery). From the 80+ tables, 30+ tables will be refreshed every 1 hr from their source systems and remaining will be once a day. Now the target table should be refreshed every 1 hr as soon as data is staged and the target table will have 350+ columns and hourly source data volume will be 40M ( 90% data will be changes to existing data and 10% or less will be new data) and over all table volume will be around 3B records. Any best practices or suggestion on designing transformation for this scenario ? Thanks,
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