AI and Machine Learning
Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.
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[Article] Kickstart your organization’s ML application development flywheel with the Vertex Feature Store
Hey Folks! This article, written by Anand Iyer (Product Manager) and Kaz Sato (Developer Advocate), was originally published on the Google Cloud Tech Blog. For more details on kick-starting your AI/ML flywheel, you can follow the tutorials and getting started samples in the product documentation. We often hear from our customers that over 70% of the time spent by Data Scientists goes into wrangling data. More specifically, the time is spent in feature engineering -- the transformation of raw data into high quality input signals for machine learning (ML) models -- and in reliably deploying these ML features in production. However, today, this process is often inefficient and brittle.There are three key challenges with regards to ML features that come up often: Hard to share and reuse Hard to serve in production, reliably with low latency Inadvertent skew in feature values between training and serving In this blog post, we explain how the recently launched Vertex Feature Store helps addr
Google Cloud is partnering with Coursera, on a new ML Academy to help us sharpen our machine learning (ML) skills and learn about the latest ML technologies from Google Cloud at no-cost. The academy has three core components for us to take advantage of in July and August:Join the ML Academy webinar to get started, on July 22.Image Source: Google Cloud Blog
Hello AI and ML Group! As people around the world are wrapping up their work week, let’s take a moment to celebrate the wins (both big and small). Feel free to share:Your personal victories this week Any company victories you would like to share out General achievements that this group can all get behind
The new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data is here.Depending on whether or not the training data is time series, users can now detect anomalies in training data or on new input data using a new ML.DETECT_ANOMALIES function (documentation), with the following models: Autoencoder model, now in Public Preview (documentation) K-means model, already GA (documentation) ARIMA_PLUS time series model, already GA (documentation) The function runs against time-series data using ARIMA_PLUS models using AUTOENCODER and KMEANS models.Read the article hereGoogle Cloud Blog
NVIDIA has announced a partnership with Google Cloud to establish the industry’s first AI-on-5G Innovation Lab.I wonder which 5G and AI applications will be created by this collaboration.Read the article hereDatacenter News ASIA
Establishing a mature MLOps practice to build and operationalize ML systems can take years to get right.As you start your MLOps journey, you might not need to implement all of these processes and capabilities.To help ML practitioners translate the framework into actionable steps, this blog post highlights some of the factors that influence where to begin, based on our experience in working with customers.You can get started with MLOps using the following Vertex AI resources: MLOps: Continuous delivery and automation pipelines in machine learning Getting started with Vertex AI Best practices for implementing machine learning on Google Cloud Google Cloud Blog
Just read this article. It is about Google Cloud’s new Visual Inspection AI solution, which has been purpose-built for the industry with Google Cloud’s best artificial intelligence (AI) and computer vision technology to solve this problem at production scale. It can run on-premise, it has a short time to value, and uses Google’s Computer Vision and ML for the inspection tasks.What do you think? Image Source: Google Cloud Blog
In this episode of #AskGoogleCloud, Developer Advocates Stephanie Wong, Dale Markowitz and Sara Robinson answer your burning questions about AI/ML, including the difference between Vertex AI and Cloud AI Platform, how to do ML on a budget, and more.AI and ML: #AskGoogleCloud Live Q&A
Expand your ML knowledge and sharpen your skills through this hands-on Google Cloud ML Academy lab co-hosted by Coursera and designed for ML Engineers and Data Scientists like you. As a bonus for attending, you'll receive an exclusive Coursera training offer and an invitation to join the ML and AI skills challenge. Register here for this event on June 22, 2021 : https://cloudonair.withgoogle.com/events/ml-lab
As Google Cloud says: “We may not know what the future holds, but we can deliver innovations to help us prepare for it. Join us to learn how to apply groundbreaking machine learning technology in your projects, keep growing your skills at the pace of innovation, and boost your career.”The Applied ML Summit is part of a Summit series. We had the Data Cloud Summit where we learned about Datastream, Dataplex, Dataflow Prime, Analytics Hub. Next are the Digital Manufacturers Summit, the Security Summit and the last is the Retail & Consumer Goods Summit. The Applied ML Summit has 3 tracks:Discovering MLOps Technical Tutorials Exploring Cutting-Edge Innovation Advanced MLOpsI’ll be watching the first one. Because I want to hear what Sara Robinson, Zack Akil, and Kaz Sato have to say.What about you? Will you join any of these summit series?Image source from Google Cloud OnAir
The Google Cloud AI Platform team has been heads down the past few months building a unified view of the machine learning landscape. This was launched at Google I/O as Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models.What is it? How good is it? What does it mean for data science jobs?Read it here. Do you want to learn how to Manage ML data sets with Vertex AI? Read this article. Looking for the inside scoop on Vertex AI? In this video, Priyanka Vergadia breaks down how this toolset supports your entire ML workflow from data management to predictions. And in this video how to manage ML datasets with Vertex AI.Learn how Vertex AI can be used for your ML projects.https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-overview You can read more on Google Cloud Docs https://cloud.google.com/vertex-ai/docs/start/ai-platform-users and on the Google Cloud products https://cloud.google.com/vert
NVIDIA recently unveiled NVIDIA Base Command Platform, a cloud-hosted development hub that lets enterprises quickly move their AI projects from prototypes to production.Google Cloud plans to add support for Base Command Platform in its marketplace to deliver a true hybrid AI experience for customers later this year.Read the article here.
Some of the most important data at your company isn’t living in databases, but in documents, and marketing assets. Yet most companies are still manually writing copy and are reliant on guesswork to get consumers to click or share. Organizations are also leaving heaps of value on the table in the form of new and better social marketing that can be unlocked with artificial intelligence applied to the process. In this episode, we chat with Kate Bradley Chernis, CEO of Lately.ai to learn what she thinks about life, music, innovation, and how her company is reinventing social marketing with artificial intelligence.Go Listen & Sharehttps://thatdigitalshow.withgoogle.com/post/episode-24-the-convergence-of-social-media-and-ai/ How can AI help in your writing? What’s your favorite 80’s movie?
Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are the leading cloud providers by a long shot. Though late to the party, GCP has seen robust growth over the years.Here is how GCP offers more benefits than AWS, Azure: Tensor Processing Unit (TPU) Vertex AI Open-source Speech and translate APIs AutoML Read more here
The Google Cloud AI Platform team has been heads down the past few months building a unified view of the machine learning landscape. This was launched at Google I/O as Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models.What is it? How good is it? What does it mean for data science jobs?Read it here.
Google Cloud’s whitepaper overview of the MLOps life cycle, MLOps processes, and capabilities and why they’re important for successful adoption of ML-based systems. It also deep dives into concrete details of running a continuous training pipeline, deploying a model, and monitoring the predictive performance of ML models.Have you read/downloaded it?
Learn how insurance companies use Google Cloud's Public Datasets program, including National Oceanic and Atmospheric Administration (NOAA)’s severe storm data, to do predictive analytics and create better risk assessments.Read the article hereImage Source: Google Cloud BlogGoogle Cloud’s Public Datasets is just one resource within the broader Google Cloud ecosystem. Do you use it? If yes, which datasets have you used?
A new feature is announced to Google Cloud’s Translation services, Document Translation, now in preview, for Translation API Advanced. This feature allows customers to directly translate documents in 100+ languages and formats such as Docx, PPTx, XLSx, and PDF while preserving document formatting.Instead of the Google-managed model, you can also use your own AutoML Translation models to translate documents. The new Document Translation feature translates business documents quickly and easily with the SOTA translation models and also combines Translation API Advanced features to easily control custom translations through a glossary or models you have trained on AutoML.Image Source: Official Google Cloud Blog
Hi all, Was doing some reading on the Cloud Blog today and I found the below article. It dives into how the Golden State Warriors are using ML and data analytics to put a better product on the court. It outlines how they built integrated data pipelines and use dbt to do data transforms. Interesting read! https://cloud.google.com/blog/products/data-analytics/warriors-use-on-court-data-for-competitive-edge
Attention Transformers or Transformers Attention?Is it the same? What is Attention? What are the Transformers?Once again Dale helps us understand these buzz words!https://daleonai.com/transformers-explained Image Source: Dale on AI (daleonai.com)Although Google Cloud has the Cloud Natural Language with the: AutoML Natural Language Natural Language API Healthcare Natural Language AI it’s always good to know how things work. Do you agree?
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