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AI and Machine Learning

Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.

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Introducing the AI & ML Moderators

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iliasC2C Team Member

published in AI and Machine Learning

Introducing the AI & ML Moderators

Let us introduce you to our AI & ML Moderator: Markus KoyUsername: markuskBio:Markus is the founder of thefluent.me and a Google Build Partner and AI enthusiast based in Vancouver, British Columbia. Markus has 18 years of experience as a Business Analyst, IT Project Manager, IT Consultant, Strategic Controller, and Developer. Born and raised in Germany, Markus immigrated to Canada in 2008. His hobbies include fishing, snowshoeing, and hiking. Fine out more about Markus and thefluent.me here: https://community.c2cglobal.com/product-updates/using-ai-to-improve-spoken-language-fluency-with-markus-koy-founder-of-thefluent-me-1628Company: thefluent.meJob Title: Founder/ Owner Mohammad AlaminUsername: malamin Bio:Mohammad is an AI/ML solutions architect at Aiiotsys. A computer training center has opened in Bangladesh. He was teaching computer skills to everyone. He graduated with honors in Computer Science from the University of Greenwich in London.Company: Aiiotsys.Job Titl

malaminModerator

posted in AI and Machine Learning

How to Diagnose issues with Anthos Clusters on VMware

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...

malaminModerator

posted in AI and Machine Learning

[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

malaminModerator

posted in AI and Machine Learning

Google podcast: Vertex AI Experiments with Ivan Nardini and Karthik Ramachandran

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/

MarkusKModerator

posted in AI and Machine Learning

AI for the Global Goals - Flood Forecasting

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

malaminModerator

posted in AI and Machine Learning

Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine

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

malaminModerator

posted in AI and Machine Learning

How to approach wildfire risk analysis with AI

Learn how to use Google Cloud and Earth Engine to visualize geospatial data for environmental risk analysis.Click on the video below to watch it in detail:

MarkusKModerator

posted in AI and Machine Learning

AI for Global Goals - Rainforest Connection

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

malaminModerator

posted in AI and Machine Learning

Latest update on enabling real-time AI with Streaming Ingestion in Vertex AI

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

malaminModerator

posted in AI and Machine Learning

Serverless replication from operational databases to BigQuery

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

malaminModerator

posted in AI and Machine Learning

Google Just Put an A.I. Brain in a Robot [Research Breakthrough]

What happens when you put an A.I. Language model in a physical robot? Lets find out.Click on video below to watch it in detail:

malaminModerator

posted in AI and Machine Learning

Get started with Looker private embedding in Google Cloud

Looker private embedding gives you a quick and simple way to embed Looker content in your host application. In this video of Embedding Looker, Jeremy Chang walk through building the Embed Content URL, creating the iframe, and enabling user access. Watch to learn how you can privately embed Looker content in an iframe!Click on the video below to watch it in detail: Chapters:0:00 - Intro0:14 - What is Looker private embedding1:04 - Knowledge check1:23 - Building the embed content URL4:40 - Create the iframe5:14 - Enable user access7:53 - Wrap up Extra Credit:Private embedding getting started docs → https://goo.gle/3BlMiza SSO Embedding with the Embed SDK getting started docs → https://goo.gle/3BHBCeD

malaminModerator

posted in AI and Machine Learning

Google Cloud inks sustainability agreement with Saudi Government & Climate Engine

Technology and AI are key enablers in today's world where sustainability and climate action are a top priority for governments and businesses. At Google Cloud, it is important for us to leverage our technology, like Google Earth Engine, that can help our customers create a sustainable tomorrow. SDAIA are paving the path for climate action in the Middle East, and Google cloud are thrilled to be part of this journey. Read the full story here Click on the link below to read it more detail:https://www.googlecloudpresscorner.com/2022-09-14-Google-Cloud-Inks-Sustainability-Agreement-with-The-Saudi-Data-Artificial-Intelligence-Authority-SDAIA-and-Ministry-of-Environment,-Water-And-Agriculture-MEWA-to-Drive-Climate-Action-in-the-Kingdom-of-Saudi-Arabia

malaminModerator

posted in AI and Machine Learning

Route Datadog monitoring alerts to Google Cloud with Eventarc

Recently, Google cloud announced third-party event sources for Eventarc, with the first cohort of third-party providers by our ecosystem partners. This is new blog post describes how to listen for events from one of those third-party providers, Datadog, and route them to a service in Google Cloud via Eventarc. Click on the link below to read it more detail:https://cloud.google.com/blog/topics/developers-practitioners/route-datadog-monitoring-alerts-google-cloud-eventarc

malaminModerator

posted in AI and Machine Learning

Google Government Summit November 15, 2022[Event]

Join government leaders to learn how the cloud can help achieve mission outcomes and unleash the potential of teams.Google Public Sector is thrilled to bring together leaders from federal, state, and local government from the United States and around the world. Secure your spot at the Google Government Summit on November 15 at District Wharf in Washington DC. Through keynotes, networking, demos and more you’ll gain new ideas of how to improve your mission work by accelerating innovation. Click on the link below and complete your registration for this event:https://cloudonair.withgoogle.com/events/government-summit-2022

malaminModerator

posted in AI and Machine Learning

Boosting Speech-to-Text API accuracy in Google Cloud

Google cloud Speech -to-text support for 73 distinct languages, and 137 different local variants over 125 languages, the Speech-to-Text API allows you to quickly and accurately convert audio to text. In this video, Anu Shrivastava, Developer program engineer walk you through the best tips and tricks for lowering your word error rate when using the Speech-to-Text API to transcribe audio files into text. Watch along to learn how you can boost your automated speech recognition accuracy without having to train your own custom model.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:48 - What is Speech-to-Text API?1:11 - Speech-to-Text API quickstart demo1:47 - How do you measure output accuracy?2:53 - Tips for checking accuracy at scale3:49 - Improving accuracy with the Speech Adaption API6:42 - Wrap up Extra CreditSpeech-to-Text API Documentation → https://goo.gle/3Bo4JEQ Measuring and Improving Speech Accuracy Lab → https://goo.gle/3cOD2eR

malaminModerator

posted in AI and Machine Learning

Latest database innovations for transforming the customer experience with Google cloud

Google Cloud enables data teams to manage each stage of the data lifecycle from operational transactions in databases to analytical apps across data warehouses, lakes, and data marts, and delivers transformative data-driven experiences. Let's dive into Google Cloud latest database innovations, including the new Cloud Spanner free trial instances, Datastream for BigQuery, Database Migration Service (DMS) support for AlloyDB, and more ..Click on the link below to read it more detail:https://cloud.google.com/blog/products/databases/latest-database-innovations

malaminModerator

posted in AI and Machine Learning

Edge AI/ML acceleration for Advanced Driver Assistance Systems

With Google Distributed Cloud, you can deploy your workloads at edge locations. Watch this video to learn how this capability is helping accelerate Advanced Driver Assistance Systems for automotive customers. Edge ML/AI solutions are enabling better end user experiences for Google Cloud customers by extending the consistent and open cloud control plane to stores, factories, warehouses, and vehicles. With the GDC Edge Appliance, you’re able access insights generated on the edge in low latency and limited bandwidth environments. The GPU-based ML accelerators allow for inferencing, aggregating, processing, and transferring of data and insights to Google Cloud Storage. Click on the video below to watch it in detail:

malaminModerator

posted in AI and Machine Learning

Take your ML models from prototype to production with Vertex AI

You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall when it comes time to elevate your experiments up to production scale. Suddenly, your concerns are more than just getting the highest accuracy score.Click on the link below to read it more detail.https://cloud.google.com/blog/products/ai-machine-learning/go-from-a-notebook-to-a-production-ml-model

malaminModerator

posted in AI and Machine Learning

Learning to Walk in the Wild from Terrain Semantics-Google AI

Presenting a hierarchical framework to learn semantic-aware locomotion skills for off-road locomotion. Using less than 30 minutes of human demonstration data, the framework learns to adjust the speed and gait of the robot based on the perceived semantics of the environment. In “Learning Semantics-Aware Locomotion Skills from Human Demonstrations”, Google AI design a hierarchical learning framework to improve a robot’s ability to traverse complex, off-road environments. Unlike previous approaches that focus on environment geometry, such as terrain shape and obstacle locations, Google AI focus on environment semantics, such as terrain type (grass, mud, etc.) and contact properties, which provide a complementary set of information useful for off-road environments.Click on the link below to read it more detail.https://ai.googleblog.com/2022/09/learning-to-walk-in-wild-from-terrain.html

malaminModerator

posted in AI and Machine Learning

Twitter: Helping customers find meaningful Spaces with AutoML

Twitter is leveraging AutoML to improve its customer experience by making Spaces recommendations that are relevant to individuals instead of ranking Spaces by order of popularity.Rafa Carvalho, Senior Customer Engineer, Machine Learning, Google Cloud, and Diem Nguyen Senior Machine Learning Engineer/Data Scientist, Twitter wrote an article based on AutoML implementation on Twitter Spaces recommendations summarized the following key points. Setting the stage for building new features with limited ML resources Increasing customer engagement at speed with accurate ML predictions Powering new use cases with hands-off ML frameworks Click on the link below to read it more detail: https://cloud.google.com/blog/products/ai-machine-learning/twitter-leveraging-automl-for-spaces-recommendations

malaminModerator

posted in AI and Machine Learning

How Let’s Enhance uses NVIDIA AI and GKE to power AI-based photo editing

Let’s Enhance, a computer vision startup with teams across the US and Ukraine, set out to solve with AI. The Let’s Enhance platform improves the quality of any user-generated photo automatically using AI-based features to enhance images through automatic upscaling, pixelation and blur fixes, color and low-light correction, and removing compression artifacts — all with a single click and no professional equipment or photo-editing chops. Click on the link below to read it more detail.https://cloud.google.com/blog/products/ai-machine-learning/innovative-ai-for-beautifying-images-using-nvidia-gpus-and-gke

malaminModerator

posted in AI and Machine Learning

Transportation leaders share real-world stories of digital transformation with Google Cloud

ITS World Congress and a session with these amazing individuals. Join with Google Cloud on September 18 to hear from DOT leaders from Utah Department of Transportation (UDOT), Hawaii’s Department of Transportation Highways Division (HIDOT), Colorado Department of Transportation (CDOT) and District Department of Transportation (DDOT) as they share their real-world stories of digital transformation with Google Cloud. Topics will range from deploying advanced analytics platforms, climate resilience, safety analytics, data driven equity, going paperless and more.Also, stop by room 304C to meet with Google Cloud Public Sector leadership team and click on the link below to read it more detail: https://cloud.google.com/blog/topics/public-sector/transportation-leaders-share-real-world-stories-digital-transformation

malaminModerator

posted in AI and Machine Learning

Understand text faster with Vertex AI's AutoML NLP

Did you know that with AutoML you can train a model with image, text, tabular, or even video data? In this video of Deploying AI, Developer Programs Engineer Anu Srivastava shares an example of Vertex AI's AutoML with Natural Language Understanding for text data. Watch to learn how you can train a custom model to recognize content that you care about in text.Click on the video below to watch it in detail:Chapters:0:00 - Intro0:30 - Example of Natural Language Understanding for text data0:50 - Use cases rundown2:10 - When to use pre-trained Natural Language API2:49 - How to build this model4:08 - Checking the model4:43 - Samples5:07 - Using model from phone or web app5:33 - Wrap upExtra Credit :AutoML Natural Language Beginner's guide → https://goo.gle/3RA31FP

malaminModerator

posted in AI and Machine Learning

Building a highly available Cloud Run app with Cloud Spanner

When building a business critical serverless application, high availability and multi-regional databases guarantee reliability. If one region goes down, you can send all of your traffic to a secondary region to prevent failure. Watch along and learn how to run your application’s database on Cloud Run in multiple regions with Cloud Spanner. Click on the video below to watch it in detail: Chapters:0:00 - Intro0:25 - What is a multi-regional cloud database?1:00 - Eliminate the single point of failure1:44 - Why choose Cloud Spanner?2:35 - How to set up a Spanner database3:41 - What if there is no regional configuration for my area?4:16 - How much does it cost to run a multi-regional configuration?5:39 - Do I need to update my application code for Cloud Spanner?6:05 - Summary of Cloud Spanner6:43 - Regional vs. multi-regional configurations7:03 - Wrap up Extra Credit:Speed up your Cloud Run service by hosting it closer to your users → https://goo.gle/3CUXAwU Serving traffic from multiple r

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