AI and Machine Learning
Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.
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Alisa Goldstein, Senior Product Manager, Cloud Alerting and John Brier, Group Product Manager, Cloud Operations Suite will speak about Transition your operations management from reactive to proactive with forecasting from Google Cloud Alerting. Join this event to learn how you can set notifications for up to 7 days with advanced notification so you can act before a problem occurs. Date: December 13, 2022 5:00 PM- 5:30 PM GMT Click on the link below to join this event. https://cloudonair.withgoogle.com/events/innovators-forecast-alerts
With millions of daily credit card purchases, how can you detect which transactions are fraudulent before they complete? In this video, developer advocate Billy Jacobson shows how we can use big data processing tools, machine learning, and the scalable Bigtable database to detect fraud in milliseconds. Watch to learn how you can deploy your own fraud detection system with horizontal scalability!Click on the video below to watch it in detailChapters:0:00 - Intro0:29 - Transaction pipeline1:11 - Managing the stream of customer transactions1:41 - Cloud Pub/Sub integration with Cloud Dataflow3:01 - Pub/Sub output stream3:30 - Summary3:58 - Wrap up Credit card fraud detection using Cloud Bigtable → https://goo.gle/3tlX6toSubscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Did you ever think about how self-driving cars work?How do ML and AI make decisions themselves without human touch?How ML and AI make decisions themself without human touch. One of the biggest challenges is training ML models with appropriate data sets, but what if the ML models could teach themselves? In this episode of Architecting with Google Cloud, Debi Cabrera, Google Cloud developer Advocate chat with Harrison at Helm.ai about how they help companies scale their capabilities with ML models.Check out this video so the next time you see a self-driving car you’ll know what’s happening under the hood! Click on the below to watch it in detail. Chapter0:00 - Intro0:48 - Helm.ai overview1:37 - Supervised vs unsupervised learning (deep teaching)3:24 - Real-world example of unsupervised learning4:50 - Can the software be used on more than cars?5:47 - Helm.ai architecture12:14 - Future technical enhancements14:18 - Wrap Up Architecting with Google Cloud playlist → https://goo.gle/Architect
Machine learning pipelines are increasingly being used by organizations to streamline and scale their ML workflows. However, managing these pipelines can be difficult when an organization has multiple ML projects and pipelines in various stages of development. To address this, Google cloud developer advocates suggested that a method be developed to build on DevOps concepts and apply them to this ML-specific problem.In this post, Google Cloud developer advocates share some best practices for managing the codebase for your ML pipelines based on their work with top Google Cloud customers and partners.Click on the link below to read it in detail:https://cloud.google.com/blog/topics/developers-practitioners/best-practices-managing-vertex-pipelines-code
Every organisations face three key challenges when it comes to unlocking the value of data:1. Data keeps growing at a fast pace and multi-format2. Data goes beyond SQL3. Data needs to reach everyone in the organisationJoin this webinar to learn how BigQuery, a serverless, cost-effective enterprise data warehouse, makes a difference in tackling those roadblocks – and realise a real competitive advantage through the pervasive use of data analytics and AI.Date: November 23, 2022 9:30 AM- 10:00 AM GMTClick on the link below to join this event.https://cloudonair.withgoogle.com/events/data-analytics-ai
Google Data Cloud Live is coming to Vancouver and will feature technology leaders and data professionals who will share insights on how you can capitalize on the next wave of data solutions. You’ll have the opportunity to learn about the many ways that you and your organization can make smarter decisions and solve complex challenges with key innovations in AI, machine learning, and analytics. click on the link below to join this event.https://inthecloud.withgoogle.com/data-cloud-live-vancouver/register.html
Google Data Cloud Live is coming to Montréal and will feature technology leaders and data professionals who will share insights on how you can capitalize on the next wave of data solutions. You’ll have the opportunity to learn about the many ways that you and your organization can make smarter decisions and solve complex challenges with key innovations in AI, machine learning, and analytics. Click on the link below to join this event:https://inthecloud.withgoogle.com/google-data-cloud-live-montreal-22/register.html
Successful machine learning deployments can depend on the ability of infrastructure to support the performance and budget requirements of a workload. Google’s open, flexible, and scalable AI infrastructure supports a wide variety of AI workloads, enabling customers to increase velocity to production, reduce costs, and to meet changing requirements over time. In this session, we discuss how to optimize across the AI stack with the latest GPUs and TPUs, fully-managed purpose-built AI infrastructure capabilities with Vertex AI, and state of the art solutions for demanding workloads. You'll also hear about how Uber, Cohere, Credit Karma, Arbor Biotechnologies and other enterprises are leveraging Google Cloud's AI Infrastructure to innovate, accelerate deployment, and drive business value.Speakers:Click on video below to watch it in detail: Extra Credit:Optimize training performance with Reduction Server on Vertex AI → https://goo.gle/3SE2VNX All sessions from Google Cloud Next → https://g
Google Cloud Next starts this week, and features over a dozen sessions dedicated to helping organizations innovate with machine learning (ML) and inject artificial intelligence (AI) into their workflows. Whether you’re a data scientist looking for cutting-edge ML tools, a developer aiming to more easily build AI-powered apps, or a non-technical worker who wants to leverage AI for greater productivity, here are some you can’t miss AI and ML sessions that is summarise by Google Cloud content editor. Developing ML models faster and turning data into action For data scientists and ML experts, we’re offering a variety of sessions to help accelerate the training and deployment of models to production, as well to bridge the gap between data and AI. Top sessions include: ANA204: What's next for data analysts and data scientistsJoin this session to learn how Google Cloud can help your organization turn data into action, including overviews of the latest announcements and best practices for BigQ
How does a tweet go from one person to hundreds of millions of people? How does the data process so quickly? In this video of Architecting with Google Cloud, Priyanka chats with Gary and Saurabh from Twitter about how data from over 200 million users goes through the Twitter data center and Google Cloud. Watch along and learn how data stored across tens of thousands of BigQuery tables in Google Cloud runs millions of queries each month.Click on the video below to watch it in detail:Chapters:0:00 - Intro1:39 - How does Twitter process data?2:20 - How does Twitter’s data get to Google Cloud?3:34 - Twitter’s data migration architecture4:40 - Use cases for BigQuery at Twitter6:00 - How does Twitter manage storage?7:49 - How does Twitter organize data?10:31 - Data storage hierarchy & provisioning12:06 - How does Twitter secure & monitor data?13:24 - Twitter’s challenges for data at scale14:21 - BigQuery at Twitter15:10 - Wrap up Extra Credit:Twitter Google collab on BigQuery → http
Can Google Cloud be used to search for extraterrestrial intelligence? In this episode of Serverless Expeditions, Developer Advocate Martin Omander chats with Raffy Traas, winner of the SETI Forward Award from SETI Institute about how he was able to search through radiofrequency data to find technosignatures. Watch to see how Google Cloud was used in the search for extraterrestrial intelligence!Click on the video below to watch it in detail:Chapters:0:00 - Intro0:39 - What are technosignatures?2:15 - Filtering out other signals3:29 - Telescope data analysis4:35 - How do you get the data from the telescope?6:05 - How did you process this data on Google Cloud?7:34 - What were the results?8:47 - What’s next for SETI & Raffy?10:25 - Wrap up
Learn about AudioLM, an audio generation framework that demonstrates long-term consistency (e.g., syntax in speech & melody in music) and high fidelity, with applications for speech synthesis and computer-assisted music. Click on the link the below to read it more detail.https://ai.googleblog.com/2022/10/audiolm-language-modeling-approach-to.html .
The next answers to the mysteries of life and discovery of disease treatments have never felt more attainable with these no-cost solutions to run AlphaFold on Vertex AI. Stephanie Wong uncovers how AlphaFold has revolutionized the scientific community and walks through 3 ways to get access to it. Check out the AlphaFold public dataset to run custom queries on BigQuery, experiment on a smaller protein database using Vertex AI Workbench, and scale to hundreds of experiments using Vertex AI Pipelines.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:34 - What is protein folding?1:19 - What is AlphaFold?2:58 - Considerations for running AlphaFold4:24 - AlphaFold Python notebook4:59 - AlphaFold batch inference solution on Vertex AI6:33 - Wrap up Extra Credit:AlphaFold BigQuery public dataset → https://goo.gle/3Em79pd AlphaFold notebook on Vertex AI Workbench → https://goo.gle/3ryXbcp AlphaFold batch inferencing solution on Vertex AI Pipelines → https://goo.gle/3V7OJhI
Introducing Medical Imaging Suite: a new solution to help organizations transform imaging diagnostics by making imaging data accessible, interoperable, and useful.Learn more about the tools and products that are in this suite Click on the link below to read it more detail:https://www.googlecloudpresscorner.com/2022-10-04-Google-Cloud-Delivers-on-the-Promise-of-AI-and-Data-Interoperability-with-New-Medical-Imaging-Suite
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
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
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
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
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