AI and Machine Learning
Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.
- 253 Topics
- 312 Replies
Visualization is essential for comprehending vast volumes of data. Today, we have BigQuery and Looker to analyse petabytes of data and extract insights in a sophisticated manner.But what about tracking data that is always changing?You may learn step by step how to construct a real-time dashboard using Cloud Run and Firestore by following the link below. How to build a real-time dashboard with Cloud Run and Firestore? https://cloud.google.com/blog/topics/manufacturing/building-a-mobility-dashboard-with-cloud-run-and-firestore
“Hey Google – what is the future of retail?” In retail, transformation is more than a requirement. It's a race. Hear from industry experts as they share best practices for meeting the common needs of your customers with data analytics and AI/ML.Join us and grow your skills at the pace of innovation to deliver meaningful, transformational retail business insights. Event date :24 August 2022 17:00- 19:00 BSTType: Cloud OnAirPlease check the following link to join the event:https://cloudonair.withgoogle.com/events/whats-the-future-of-retail
Google Cloud is partnering with many of the leading EdTech companies, as well as industry-leading consortiums like Ed-Fi and Unizin, to standardize educational common data models and best practices for more agile and cost-effective integration of EdTech into existing environments.Image Source: Official Google Cloud BlogDo you agree that the education landscape is changing rapidly, and EdTech has a major role to play as institutions adapt to the massive shift in learners’ preferences and expectations?
Making Friends with Machine Learning was a legendary internal-only Google course specially created to inspire beginners and amuse experts. Today, it is available to everyone! You can now enjoy it by following these links: Part 1 — Introduction to ML: bit.ly/mfml_part1 Part 2 — Life of a Machine Learning Project: bit.ly/mfml_part2 Part 3 — AI from Prototype and Production: bit.ly/mfml_part3 Part 4 — Opening the Black Box: bit.ly/mfml_part4
Michelle Carney is a computational neuroscientist turned UX researcher, whose practice focuses on the intersection of machine learning and UX. Currently, a Senior UX Researcher on Google’s Tensorflow Team, Michelle's projects focus on combining Machine Learning and UX.In this session, Michelle joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to discuss user experience and design thinking for machine learning products and how the combination is more than the sum of its parts when it comes to creating business value.Please join the video call prior to the start of the event.Register on eventbrite to receive joining link: https://bit.ly/3AZ5OTDAfter attending, you’ll knowWhy data scientists and machine learning practitioners need to start thinking more about UX; The top 3-5 things data professionals should know about UX design to move the needle for their products; How to measure the success of user experience and design thinking initiatives when it comes to ML product
The FA's Player Performance System (PPS) is a key component of Helix, the FA's application and development suite that has been hosted on Google Cloud Platform (GCP) for the past five years.The FA platform is built on various GCP tools that are linked together by a complex micro service system that is used to update the data being collected, analysed, and stored.GCP tools work to tracking FA data is a computationally intensive process because it must be cleaned, smoothed, and have external information applied to it before its value can be realised to achieve accurate result.Now lets think about advantage England: will data analytics help bring home a Trophy? Click here to read full report.
DetailsJoin us for the twenty-second Google Open Source Live event in our series; “Machine Learning Day”!Google Machine Learning experts will share updates on everything from Tools for Responsible Machine Learning, to Using Job Queueing to Optimize for Efficiency of Your Compute Resources.Throughout the event, our speakers will answer selected questions via the Live Q&A Forum. We’ll wrap up the event with an After Party.Event:Machine Learning Day on Google Open Source LiveDate:Thursday, August 4th from 9:00 am - 11:00 am PSTAgenda:9:00 Opening for Machine Learning Day on Google Open Source LiveAlexandra Bush, Head of Open Source Marketing and Community (Google Cloud)Thea Lamkin, Senior Program Manager (Google)9:03 Session 1: Tools for Responsible Machine LearningAmy Wang, Product Manager (Google)Bhaktipriya Radharapu, Software Engineer (Google)Responsible Artificial Intelligence practices should be embedded within each step of the machine learning journey. We'll introduce an open s
Free copy Gartner® names Google as a Leader in the 2021 Magic Quadrant™ for Cloud Database Management Systems
Gartner recognizes Google as a Leader in the 2021 Magic Quadrant for Cloud Database Management Systems (DBMS) for its product Google Cloud Platform. We are honored to be named a Leader and look forward to partnering with you on your digital transformation journey. In this report, we believe you will learn:Why Google Cloud was named a Leader Where the cloud database and analytics market stands and where it’s going How the vendors are evaluated on the various criteria How Google's open data platform supports your data ecosystem Free Copy download from here.
Google AI and Machine Learning are Transforming Bose Ideas in 1901: "Power of Feeling in Plants". People wondered when Google first time introduce to google AI assistant Google Duplex.Google Duplex, is an AI assistant that can conduct phone calls for you and save you the trouble of human interaction.Then, Google announced that Google Assistant on the Google Home Hub has the ability to talk to plants, in particular, tulips. It was a great wonder for Bose world. That means google shows the world the reality of the Bose statement "Power of feeling in plants" was given by Sir Jagadish Chandra Bose -1901. It is a great implementation of advanced artificial intelligence and machine learning in google tulip.Sir Jagadish Chandra Bose was a pioneering Bangladeshi scientist who demonstrated through experimentation that animals and plants have many similarities. He showed that plants are sensitive to heat, cold, light, noise, and a variety of other external stimuli.Bose devised the crescograph,
In this video, we look at the eight biggest trends in the field of AI and machine learning. We cover the innovations around natural language processing, creative AI, data centric AI, as well as the democratization of AI via simple self-service user interfaces.The question is (What are new innovations in cloud that has revolutionized the implementation of AI?)Press om this link to watch now and discuss on this page - [SITE REMOVED DUE TO MALWARE]
Celebrate Juneteenth 2022! Learn more about Juneteenth and it’s significance, watch the video and learn about how “Juneteenth developed as a jubilee celebration in Texas and then how it moved from a regional holiday to a National one; becoming what is now known as Juneteenth National Independence Day.” Spend the Day Learning- https://www.cloudskillsboost.google/paths This Hands-on Lab focuses on Identifying Bias in Mortgage Data using Cloud AI Platform and the What-if Tool https://www.cloudskillsboost.google/focuses/10903?locale=it&parent=catalog
Let’s say it’s the first day in your new job and you have lots of questions. Where is the HR department located or how do I validate parking? Wouldn’t it be great if you could just ask a chatbot rather than reading through an internal Wiki? Here is a recipe to build one: Select your reference documents and pre-process them in Vertex AI Workbench Download a model from TensorFlow Hub and register with Vertex AI Have Vertex AI process your questions and answers Use Cloud Functions for your REST endpoint Use the Speech-To-Text and Text-To-Speech APIs to allow users using their voice rather than typing Sounds complicated? Nikita Namjoshi and Zack Akil demonstrate in this 16 minutes Google Cloud Applied ML Summit video how to do just that.https://youtu.be/5iSmX8sqtx8
DIY Dialogflow CXUseful links and documentation:https://codelabs.developers.google.com/codelabs/dialogflow-cx-retail-agent#0Build a retail virtual agent from scratch with Dialogflow CX - Ultimate Chatbot Tutorials I am working on an Online course about Chatbots and would be happy to collect interesting links and more information on the implementation of Dialogflow CX.
Have you thought about using machine learning to solve a problem but you are not sure if you can solve it fast with ML? If you decide you want to give it a try, but you don’t have much time to build a model from scratch, what are your options? And finally, what if you do not have enough data to train the model? Dale Markowitz, Applied AI Engineer at Google, provides a fast overview on how to approach these questions in this 13 minutes I/O 2022 video. https://youtu.be/BjTNJSQLIeA
The Data Cloud & ML Asia Pacific Summit is coming live to your screens, this 21-22 June! See how unified data solutions and the best in #ML can unlock limitless innovation in your business. Get ready for:Real world use cases Product demos Hands-on labs Register now [LINK]
On June 14th we're hosting BigQuery ML: Easy Access to Machine Learning.Join Mike Walker and Rob Vogelbacher, AI/ML Specialist Customer Engineers at Google Cloud, for a one-hour BigQuery Machine Learning overview and deep dive including:- Why your analytics data should be in BigQuery- BigQuery ML capabilities- Demos and getting started with BigQuery MLRegister here: https://community.c2cglobal.com/events/bigquery-ml-easy-access-to-machine-learning-139
The Applied ML Summit is exactly a week away! Here are the top three reasons you should join our event: Our opening keynote: Hear from Andrew Moore, VP and General Manager of Cloud AI and Industry Solutions at Google Cloud, and Priyanka Vergadia, Developer Advocate at Google Cloud, on accelerating the deployment of predictable ML in production. Insights in practitioner sessions: Discover how data scientists and ML experts at companies like Vodafone, Glance, General Mills, H&M, and CNA Insurance are developing, deploying, and safely managing long-running, self-improving AI services with Google Cloud. The latest product announcements: Be among the first to find out about the newest capabilities across Vertex AI, BigQuery ML, AutoML, and our integrated data services.Click here to register today!
Machine Learning (ML) model code accounts for approximately 5% of the total code needed to train, deploy, and run ML models in production according to Google research. The goal of MLOps is a production-level process, similar to DevOps, but with data as a first-class citizen. Robert Crowe, Tensorflow Developer Engineer, introduces TFX, an open-source project, in this 13 minutes I/O 2022 video. If you are interested in getting started with MLOps, this video is for you.https://io.google/2022/program/8f7765f2-2357-4829-8c87-e54d81e999a0/
When developing a machine learning model, running multiple Trials of your training code with different values can be very time-consuming. Serverless hyperparameter tuning and multi-GPU setup are two services to make model training faster with Vertex AI, Google’s managed AI platform. Nikita Namjoshi demonstrates in this I/O 2022 video how to use these features. They are real time-savers.https://io.google/2022/program/934cbc5f-42bb-4f6c-99fe-4972995eb381/
Hi everyone!I'm starting to work with Google Vertex and I had this question regarding the scalability of the endpoints.When I configure an endpoint I must:1) set hardware for the training machine (n1-standard-2, for example);2) set a minimum and maximum number of computer nodes.Number 1 is mandatory and number 2 is optional (if configured, new nodes will only be instantiated with the configuration defined in item 1).What is unclear to me is when I should invest in a larger hardware configuration (1) and when I should invest in a larger number of nodes (2).Does anyone have an idea?Thank you!
The clock is ticking… don’t miss your chance to register for this year’s Applied ML Summit happening in three weeks on June 9, 2022. Our practitioner-oriented sessions will teach you how to: Streamline the process to audit, track and govern ML models Automate and monitor AI integration, deployment, and infrastructure management Train high quality ML models in minutes with AutoML Make the most of your Google Cloud investments in Vertex AI training and prediction Build reliable, standardized AI Pipelines … and more.You’ll also get to hear Andrew Moore, VP and GM for Cloud AI and Industry Solutions for Google Cloud, and Priyanka Vergadia, Developer Advocate for Google Cloud, lead a conversation with Smitha Shaym, Director of Engineering for UberAI, and Bryan Goodman, Director of AI and Cloud for Ford, on accelerating the deployment of predictable ML in production. Save your spot today and make sure to check out our site for more event updates and details!
We’re exactly a month away from our Google Cloud Applied ML Summit happening on June 9th, 2022! Join us for 18 sessions all about developing, deploying, and managing ML models at scale from the world’s leading professional machine learning engineers and data scientists. Listen to AI/ML leaders from a wide range of industries and companies including Uber, Ford, H&M, and more. Plus, you’ll get to choose your own adventure with our three different tracks designed to best meet your ML interests: Data to ML Essentials Fast-Track Innovation Self-Improving ML Register today for this digital event and view the full agenda now. We can’t wait to see you there!
Login to the community
Social LoginLogin With Your C2C Credentials
Enter your username or e-mail address. We'll send you an e-mail with instructions to reset your password.