AI and Machine Learning
Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.
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Today, as Google cloud celebrate seven years of general availability of the most automated and scalable managed Kubernetes, Google Kubernetes Engine (GKE), Google cloud present seven of the common ways that GKE helps customers do amazing things. Increases developers' productivity Developer time is at a premium. GKE provides a rich set of integrated tools to help their customers ship faster and more often. The practice of continuous integration (CI) allows developers to frequently integrate all their code changes back into a main branch, exposing failures faster by revealing issues as early as possible in the process. A CI pipeline typically produces an artifact that you can deploy in later stages of the deployment process with continuous delivery (CD). CD lets you release code at any time. The ecosystem of developer tools for GKE spans across CI and CD. Developers write, deploy, and debug code faster with Cloud Code and Cloud Shell Continuously integrate and deliver updates with Cl
Hi,I am working on Document AI solution where I extracts the texts from a group of pdf documents to document AI processors and upload some of the extracted informations to BigQuery, Basically, I have two python scripts, very long codes, in vertex ai benchmark jupyter notebook environment. Now I want to build a pipeline to automate the sequence of execution of these two python script. I have been searching the possible way to run a jupyter notebook from Vertex Pipeline which is basically Kubeflow without success. I know that I can execute python functions from vertex pipeline but what I need is to run the whole python script from vertex benchmark jupyter notebook. Any help will be very much appreciated.Regrads
In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends software engineers, architects, or data scientists should watch. The editors curate their discussions into a technology adoption curve with supporting commentary to help us understand how things are evolving.The image below shows the adoption categories:AI, ML, and Data Engineering InfoQ adoption curve - August 2022 If you would like to dig deeper into any of these trends, then I recommend bookmarking this page: https://www.infoq.com/articles/ai-ml-data-engineering-trends-2022/.
I writing here about Google Cloud's work over the past eight years to bring new levels of performance, scale, reliability, and power efficiency to Google's Jupiter datacenter network.The combination of optical circuit switching and Software Defined Networking allowed google cloud to bring flexible topologies and real-time traffic engineering to the datacenter, enabling seamless upgrades and adaptation to application and network dynamics.In an industry first, Google cloud is able to develop and deploy at-scale optical circuit switching to configure network topologies through software control. Through a building-scale, all-optical backbone, Google Cloud can now support heterogeneous technology and evolving mesh topologies, orchestrating the equivalent of thousands of logical fiber moves with no application impact and no physical presence.Jupiter serves as a key enabler for Google Cloud and dozens of planetary-scale Google services. Now google cloud is able to bring this next level of ca
Today, I’ll introuduce with Google cloud AI latest libray Rax, a library for LTR in the JAX ecosystem. Rax brings decades of LTR research to the JAX ecosystem, making it possible to apply JAX to a variety of ranking problems and combine ranking techniques with recent advances in deep learning built upon JAX (e.g., T5X). Rax provides state-of-the-art ranking losses, a number of standard ranking metrics, and a set of function transformations to enable ranking metric optimization. All this functionality is provided with a well-documented and easy to use API that will look and feel familiar to JAX users. Please check out our paper for more technical details. Click on the below link to read more details.https://ai.googleblog.com/2022/08/rax-composable-learning-to-rank-using.html
Calling all storage architects and enthusiasts. Learn how to make your storage infrastructure work harder for your business. Join our digital event to discover what’s new in storage from Google Cloud. Hear from technical experts across our Storage portfolio, see new products in action with live demos, listen to how our technology is helping customers grow, and take away new ideas that will help you take advantage of Google Cloud’s latest storage innovations. Date and time : 8 September 2022 17:00- 18:15 BST Click on the following link to join the event: https://cloudonair.withgoogle.com/events/storage-spotlight
Kaluza is a UK-based technology company that provides energy retailers with real-time billing, smart grid services, and seamless customer experiences. In this blog, Tom Mallett, Sustainability Manager, Kaluza, explains how Kaluza leverages Google Cloud to improve energy visibility throughout the company. He also explores how better emissions data informs sustainability solutions that make the world’s energy greener, smarter, and more reliable.To read more information, click the link below : https://cloud.google.com/blog/topics/sustainability/kaluza-uses-google-cloud-to-help-people-reduce-emissions
Today, most robots are found in industrial settings and have been rigorously programmed to do particular jobs. What if there was a more effective way to interact with intelligent robots so they could assist us? Together with helper robots that can handle challenging and abstract tasks like "cleaning up a spilt drink," researchers and engineers from Google Research and Everyday Robots are developing the best machine learning language models.Recently, Brian Ichter and Karol Hausman, Research Scientists, Google Research, Brain Team wrote an article based on a better way to communicate with learning robots. Please click on the following link to read more details. https://ai.googleblog.com/2022/08/towards-helpful-robots-grounding.html
Tired of tuning your parameter-heavy transformers with Hyperparameter tuning algorithms (even those as advanced as Google's Vizier)? Instead, why not use transformers to tune your Hyper Parameters?Click on the following link to read more details. https://ai.googleblog.com/2022/08/optformer-towards-universal.html
Develop an AI strategy for your data. Solve business-critical problems with technology for any level of ML expertise. IDC found that only 36% of their surveyed enterprises succeeded in putting completed models into production. Nearly half of the remainder hadn’t even made it past proof of concept. We’ve learned from Google’s years of experience in AI development how to overcome common challenges. With successful AI strategies, we’re seeing customers from many industries and varying levels of ML expertise enhance and solve business-critical problems with AI-driven decision making. Event Date and Time: 18 August 2022 17:00- 17:30 BST For more information and to register for the event, please click here.https://cloudonair.withgoogle.com/events/data-to-ai-webinar
The use of matrix factorizations in recommendation systems is widely. If you want to quickly and easily develop a solution to provide excellent recommendations to your clients, then you will get a good and simple starting point from this video: The above video contains following content : 00:00 Introduction00:49 Pipeline01:28 Dependencies02:20 Data02:55 Why reservations04:10 Create slots08:31 Delete slots09:00 Train model10:40 Deployment12:24 Serving12:47 Running Pipeline14:40 Deployment16:04 Recommendations Extra supporting credit: Check out the two Google articles if you're new to matrix factorization and BigQuery ML.Recommendations with implicit feedback https://cloud.google.com/bigquery-ml/docs/bigqueryml-mf-implicit-tutorial Recommendations with explicit feedbackhttps://cloud.google.com/bigquery-ml/docs/bigqueryml-mf-explicit-tutorial Note:The data used in the video demonstration and the Google BigQuery ML examples are identical. No need to reinvent the wheel in this case.
Doesn't real-time supply chain visibility sound great? To provide customers with complete data for safe, secure, and on-time freight, Cargo Signal is working with Google Cloud and Confluent.Google cloud help confluent to removes obstacles from the IoT data capture and analysis pipeline . Also Cuts potential data integration issues in half through managed data streaming services on Confluent Cloud and maximizes speed-to-market and accelerates issue resolution through flexible development infrastructure in GKE.Find out more, click on the following link:https://cloud.google.com/customers/cargo-signal
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
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
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
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
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-
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/
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.
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
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