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In 2024, security teams should focus on improving their ability to detect and respond to threats powered by generative AI, zero-day vulnerabilities, nation-state activities, cyber crime, and malware. They should also be prepared for increased cyber activity related to elections and other major global events.The Google Cloud Cybersecurity Forecast 2024 provides insights on threats and trends from security experts across Google Cloud.Click on the link below to read the report from pdf.https://services.google.com/fh/files/misc/google-cloud-cybersecurity-forecast-2024.pdfhttps://cloud.google.com/resources/security/cybersecurity-forecast
In the realm of artificial intelligence, machine learning has emerged as a transformative force, revolutionizing industries and shaping the future of technology. However, building effective machine learning models can be a daunting task, requiring specialized expertise and extensive knowledge of algorithms and techniques. This is where automated machine learning, or AutoML, comes into play.AutoML is a set of tools and techniques that automates the process of building machine learning models, making it accessible to a wider range of users, including those without extensive machine learning experience. AutoML simplifies the process by automating tasks such as data preparation, feature engineering, model selection, hyperparameter tuning, and model evaluation.Why AutoML Matters: AutoML has gained significant traction in recent years due to several compelling advantages:Accessibility: AutoML democratizes machine learning, making it accessible to non-experts and enabling businesses to tap in
Unleash the transformative power of AI in your business. Register now for Google Cloud Transform: The Future of AI-Powered Apps webinar.Join with this event in your time zone to hear speaker who will explore the latest advancements in AI and how they can revolutionize your business operations. Discover how AI can enhance productivity, accelerate innovation, and unlock new market opportunities.During this webinar, you will learn:How to identify and implement AI solutions that align with your business goals Practical strategies for integrating AI into your existing workflows and processes Real-world examples of how businesses are leveraging AI to achieve success Air times:North & South America: November 15: 9:00 AM PTEurope, Middle East, Africa: November 15: 9:00 AM BSTAsia Pacific: November 16: 12:00 PM SGTClick on the link below to join this event.https://cloudonair.withgoogle.com/events/transform-the-future-of-ai-powered-apps
Mark your calendars for December 7th, 2023, third annual Women in ML Symposium! 🎉✨This symposium is a day of inspiration, learning, and networking, exploring the latest tools and techniques in ML. 🚀🔬Whether you're a professional, an enthusiast, or simply curious about the world of ML, this event is for you! Lineup of experts will share their insights, experiences, and innovative applications, providing you with invaluable knowledge to apply in your own ML journey. 📚💡https://youtu.be/F3d7THYFBe4The Women in ML Symposium is an inclusive event, open to everyone. Connect with like-minded individuals, expand your network, and be inspired by the incredible achievements of women in ML. 💪🌟Don't miss out on this fantastic opportunity to be part of an empowering community! 📅🔜https://aidevelopers.withgoogle.com/events/wiml-symposium-2023/register
Artifact Registry is now generally available (GA) with two new features: remote repositories and virtual repositories. GA means that the features are fully supported and available to everyone. Also, Artifact Registry remote repositories now support authentication to Docker Hub upstream repositories. To create a Docker Hub remote repository, take the quickstartThese two features make Artifact Registry a more versatile repository for developer artifacts. Remote repositories store artifacts from external sources, such as Docker Hub, Maven Central, PyPI, Debian, and CentOS. They also act as a proxy for the external source, giving you more control over your external dependencies. Virtual repositories act as a single access point to download, install, or deploy artifacts in the same format from one or more upstream repositories.An upstream repository can be a standard or remote Artifact Registry repository. These features give you great flexibility when configuring artifact storage.To get st
This demo features multiple Google Cloud products including Matching Engine, VertexAI PaLM to combine the functionality of retrieval augmentation and conversational engines to create a question answering system where the user can ask a question and the LLM will use it's given context to answer the question.Click on the link below to watch the demo and documentation.https://llmops-demos-frg.web.app/ Here is the GitHub link below:https://github.com/danielfrg/gcp-llm-retrieval-augmentation
LangChain is an open-source framework for building Generative AI applications powered by language models. Vertex AI PaLM 2 foundational models are now integrated with the LangChain Python SDK, making it easy to build applications on top of Vertex AI PaLM models. Click on this video below, to learn how to build a Generative AI application using Vertex AI PaLM and Scaling and Automating Palm 2 with LangChain.
Transitioning from gen AI prototypes to production-ready apps can be challenging. In this session, a Gen AI specialist will walk you through the recommended design patterns, as well as deployment, safety, and operational considerations you need to bring your Gen AI vision to users at scale.Key Takeaways:Learn about the recommended design patterns for gen AI production-ready apps Understand the deployment, safety, and operational considerations for gen AI apps Get tips for bringing your Gen AI vision to users at scaleClick on the link to join this event.https://cloudonair.withgoogle.com/events/300-taking-gen-ai-app-from-prototype-to-production
Conversational AI is a rapidly growing field with the potential to transform the way we interact with technology. By enabling machines to understand and respond to natural language, conversational AI can create more personalized, engaging, and efficient experiences for users.There are a variety of techniques that can be used to create conversational experiences, including retrieval augmented generation, and Vertex AI Search and Conversation.Retrieval augmented: Retrieval augmented generation is a technique that combines machine learning with natural language processing to generate text that is both informative and engaging. It works by first retrieving relevant information from a knowledge base, and then using machine learning to generate text that is tailored to the user's request.Vertex AI Search: Vertex AI Search and Conversation is a cloud-based platform that provides a variety of tools for building and deploying conversational AI applications. It includes a search engine that can
Join this Google Cloud event for two days of immersive learning on the latest cloud technologies, including Gen AI, analytics, data cloud, app/infra modernization, and more. You'll participate in technical labs, interact with Google experts, and connect with the wider cloud community.Register now for the Cloud Technical Series, for November 23-11-2023 to -24-11-2023.https://cloudonair.withgoogle.com/events/cloud-technical-series-apac-q4-2023
Cortex & GenAI Hands-On Lab: Build a Generative AI App in 90 MinutesThis interactive workshop will teach you how to use Cortex and GenAI to build a generative AI app on Google Cloud. You'll learn about the Cortex data model, Generative AI use cases, and how to identify business opportunities for Generative AI. You'll also build a simple GenAI app in the lab.Click on the link below to watch the video.
Model Garden is a comprehensive platform designed to empower users to discover, test, customize, and deploy Google-developed and select open-source AI models. It serves as a gateway to the vast array of AI models and APIs available on Vertex AI, enabling users to harness the power of artificial intelligence for their project specific needs.New Models added on Model Garden:ImageBind: Multimodal embedding model. Vicuna v1.5: LLM finetuned based on llama2. OWL-ViT v2: SoTA Open Vocabulary Object Detection model. DITO: SoTA Open Vocabulary Object Detection model. NLLB: Multi-language translation model. Mistral-7B: SoTA LLM at small size. BioGPT: LLM finetuned for biomedical domain. BiomedCILP: Multimodal foundational model finetuned for biomedical domain.Click on the link below to explore models in Model Garden:https://cloud.google.com/vertex-ai/docs/start/explore-models
Google Cloud has released an updated guide to assist cloud architects in comprehending the deployment archetypes available for their cloud workloads: zonal, regional, global, multi-regional, hybrid, and multicloud.The guide delves into the specific use cases and design considerations for each archetype, empowering architects to make informed decisions when selecting the most suitable archetype for their cloud architecture. Additionally, a comparative analysis is provided to facilitate the selection process.Click on the link below to read it more details.https://cloud.google.com/architecture/deployment-archetypes
Hi C2C community!AI is already having a profound impact on how organizations operate. The power of AI allows you to reimagine what you do, how you do it and who you do it for. For many companies it feels like they’re just one step away from using AI to start solving real business problems — they just need to activate their data.Google Cloud has a robust portfolio of platforms and tools for storing, transforming, and gaining insights from your data, and that can be activated for AI. In this blog, we will summarize the key innovations to our Data and AI Cloud in 2023 across three strategic areas.Interconnecting all your data - structured and unstructured, in any format, across all locations. Bringing AI to your data - securely and quickly build AI models with all your data. Boosting productivity - helping all data teams to analyze data, generate code and optimize data workloads.Register for the upcoming webcast on Nov 13th to get a snapshot of our plans and investments in BigQuery, Strea
Google Cloud is pleased to introduce the GCE Rescue tool, which allows you to boot a faulty instance in rescue (maintenance) mode and fix the instance boot.GCE Rescue works by creating a temporary boot disk from a Linux image and attaching it to the faulty instance. This allows you to access the instance's files and filesystem and perform any necessary repairs. Once the repairs are complete, you can detach the temporary boot disk and reboot the instance in normal mode.To use GCE Rescue, you will need to know the name and zone of the faulty instance.You can check the video below for how you can use GCE Rescue tool: Also, You can check the following link to see the GCE Rescue repository :https://github.com/GoogleCloudPlatform/gce-rescueLife-raft rescue method:https://cloud.google.com/compute/docs/disks/recover-vm
Does Google offer a solution that performs speech analytics on transcribed text that supports API calls. The solution will interview transcripts and provide sentiment analysis and insight on the users conversation (stutters, “Uhm”, seems unprepared). Generate a report and then graph it.
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 .
🚀🤖 Join Google Cloud experts for a three-session Gen Al Bootcamp which guide you through the process of getting started as a Gen AI developer. In this transformative journey, you'll learn the fundamentals of generative AI and gain hands-on experience to build your very first app. 📚💻 Register at:https://cloudonair.withgoogle.com/gen-ai-bootcamp Together, let's shape the future of technology through the limitless possibilities of Generative AI. 🌍🚀
Google cloud interactive Gen AI Navigator provides tailored recommendations to help you and your business achieve your goals. It provides personalized recommendations based on your organization's specific needs and goals. Simply answer a few quick questions about your strategy, infrastructure, and skills to get started. Strategy The first section of the Gen AI Navigator focuses on your organization's strategy for integrating generative AI into business operations. It assesses your methodologies around generative AI to frame your organization's approach.Some questions you may be asked in this section include:What are your goals for using generative AI? How do you plan to measure the success of your generative AI initiative? What are your biggest challenges to adopting generative AI? What are your current AI capabilities?Infrastructure The second section of the Gen AI Navigator focuses on your organization's infrastructure. It assesses your data and hosting infrastructure, as well as s
Learn how to secure your cloud environment with Google Cloud security experts. Google Cloud security experts will teach you how to use cloud-native security technologies to improve your ability to see your assets, find vulnerabilities and configuration errors, identify security risks, detect threats, and meet compliance requirements.Click on the link below to join this workshop. https://rsvp.withgoogle.com/events/google-cloud-security-workshop-series
Hello, I am trying to run the sample colab notebook (this notebook) for deploying LLaMa 2 from the model garden. Specifically, I am trying run the cell which fine tunes using PEFT. When I run this section of the notebook, I am getting the following error:huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/gcs/llama-train-1/peft/base_model/llama2-7b-chat-hf'. Use `repo_type` argument if needed. I am confused as to the source of the error. I have made very few edits to the notebook besides inputting what is asked for -- what I am doing wrong here?
Best Practices for Migrating On-Premises Workloads to Google Cloud:★ Share your experiences, tips, and challenges when it comes to migrating your on-premises workloads to the Google Cloud platform. Let's discuss what worked well and any lessons learned along the way!
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