Google Cloud Platform Solutions and Technologies
Solve your business challenges and prepare your team with Google Cloud solutions, products, and services.
Migrating existing workloads or deploying new workloads in the cloud provides the opportunity to improve protection against attacks. Come learn about approaches to defend your network perimeter and prevent lateral movement of threats. Tracy Jiang, Senior project manager, Google Cloud will discuss the unique capabilities of Google Cloud firewalls that can deliver a no-compromise zero-trust posture. She will also discuss how firewall insights and Cloud IDS can monitor for gaps in protection and prevent attacks. Click on the following video to watch it in detail : Video insights:
Are you moving your organization from on-premise PKI services to Google Cloud? With Certificate Authority Service (CAS), you can achieve zero-trust policies for secure communication with your CAs. Watch along and learn how CAS provides fully managed, cloud-native CAs for Google Cloud Platform customers in conjunction with VPC Service controls to maintain zero-trust in the cloud.Click on the following video to watch it in detail : 0:00 - Intro0:33 - Today’s on-premise PKI deployments0:54 - Certificate Authority Service (CAS) + Virtual Private Cloud (VPC) SC1:26 - Virtual Private Cloud service controls2:16 - VPC access controls2:57 - What is Access Context Manager (ACM)?3:13 - CAS & VPC SC perimeter example4:51 - Wrap up Extra Credit:Access Context Manager → https://goo.gle/3cfBJ7V Endpoint verification → https://goo.gle/3PdNsTa VPC Service Controls→ https://goo.gle/3aKg5bu
Creating a better world can be a reality with AI, and putting that at the core of what you do is the key to being a successful innovator. In this video below, Andrew Moore, General Manager, AI & Industry Solutions, Google Cloud, will talk about the mindset required to build a successful AI business, why the commitment to responsible AI is critical, and how to embed yourself within an ecosystem of partners who carry the same level of commitment to your cause and approach. Please click on the video to watch more details.
Hi! I wanted to share just a quick note of welcome to this new group. We’ve heard some community interest in a dedicated space to discuss this specific use case, so we’ve created this group with the support of our Foundational Gold Partner: Managecore. All are welcome to start conversations, ask questions, and contribute. Managecore is here to help ensure there is regular activity, as well as a team of experts to answer questions and contribute to conversations. To get started, make sure you have joined the group: that’s what enables your ability to start and reply to topics. More details and activities soon to come. Until then... Reply below with what you’d most like to see from this group!
When you build a Machine Learning (ML) product, consider at least two MLOps scenarios. First, the model is replaceable, as breakthrough algorithms are introduced in academia or industry. Second, the model itself has to evolve with the data in the changing world. You can handle both scenarios with the services provided by Vertex AI. For example: AutoML capability automatically identifies the best model based on your budget, data, and settings. You can easily manage the dataset with Vertex Managed Datasets by creating a new dataset or adding data to an existing dataset. You can build an ML pipeline to automate a series of steps that start with importing a dataset and end with deploying a model using Vertex Pipelines. This blog post Chansung Park, ML Google Developer Expert shows you how to build this system. You can find the full notebook for reproduction here. Many folks focus on the ML pipeline when it comes to MLOps, but there are more parts to building MLOps as a “system”. In
Remy Welch, Customer Engineer, and Michael Entin Software Engineer from Google Cloud recently summarized the best practices of spatial clustering on BigQuery. Their explanation is excellent. I share here that this might help those who want to reduce the cloud cost and increase the BigQuery performance. Most data analysts are familiar with the concept of organizing data into clusters so that it can be queried faster and at a lower cost. The user behavior dictates how the dataset should be clustered: for example, when a user seeks to analyze or visualize geospatial data (a.k.a location data), it is most efficient to cluster on a geospatial column. This practice is known as spatial clustering, and in this blog, we will share best practices for implementing it in BigQuery (hint — let BigQuery do it for you). BigQuery is a petabyte-scale data warehouse that has many geospatial capabilities and functions. In the following sections, we will describe how BigQuery does spatial clustering out
Cloud-native has become synonymous with container-native, and as a result, a Kubernetes strategy is crucial to the success of IT given how quickly IT requirements are changing to meet shifting customer demands. Join this Google Cloud Innovator event to discover how to improve your containerized apps with fully managed Google Kubernetes Engine solutions, with in-depth analyses of integrated day 2 operating solutions and capabilities and how to execute your workload on both ARM and x86 processors. Date and Time: 13 September 2022 18:00- 19:00 BST Speaker: Please click on the link below to join this event. https://cloudonair.withgoogle.com/events/innovators-run-apps-on-kubernetes
Managed notebook environments make it easier, faster, and more cost-effective to get high-quality models into production without having to set up infrastructure or install libraries. In this event, Nikita Namjoshi will show a demo how to use Vertex AI to get batch and online predictions, monitor deployed model quality over time, and use the Vertex AI Python SDK to upload models to Vertex AI Model Registry and deploy to an endpoint with little code. Date and Time: 30 August 2022 17:00-17:30 BSTSpeaker: Nikita Namjoshi Developer Advocate. Please click on the link below to join this event. https://cloudonair.withgoogle.com/events/innovators-deploy-ml-model-vertex-ai
Today, Google Cloud announces the public preview of Explanations based on examples of Vertex AI, a new feature that provides usable explanations to mitigate data challenges like mislabelled examples. Explanations based on examples Vertex AI, data scientists can quickly identify misplaced data, improve datasets and involve stakeholders more effectively in decisions and progress. This new feature takes the model refinement riddle games, allowing you to identify problems faster and accelerate the value time.Artificial intelligence (AI) is a potent tool for extracting more value from data since it can automatically understand patterns that people cannot.A high-performing model starts with high-quality data, but frequently datasets have problems, including inaccurate labels or murky examples, which affect the performance of the model.Enterprises always struggle with data quality issues; label errors can even be found in some datasets used as ML standards.As a result, it is frequently diffic
I've got listed a few resources to assist you in developing and honing your data science, machine learning, and artificial intelligence skills on Google Cloud. This is list recommend by Google Cloud developer advocate Nikita Namjoshi and Polong Lin.Some of the resources that a Data Analyst, Data Scientist, ML Engineer, or Software Engineer might find most interesting have been categorised based on jobs. Data Analyst From data to insights, and perhaps some modeling, data analysts look for ways to help their stakeholders understand the value of their data.Data exploration and Feature Engineering [Guide] Exploratory Data Analysis for Feature Selection in Machine Learning [Documentation] Feature preprocessing in BigQuery Data visualization [Guide] Visualizing BigQuery data using Data Studio [Blog] Go from Database to Dashboard with BigQuery and Looker Data ScientistAs a data scientist, you might be interested in generating insights from data, primarily through extensive exploratory da
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
The Google Cloud team has given hard work on virtual machine scaling the service, refining virtual machine detection capabilities, and preparing google cloud's next major feature set. VMTD in general availability has been scaled to support significantly more frequent scanning across a tremendously large number of instances. Scaling the scanning of memory from the Google Cloud Compute Engine (GCE) fleet has posed unique challenges, and google cloud invested in caching scan results to enable more frequent scans of smaller – but more important – sections of memory.Today Google cloud are announce that their unique, first-to-market detection capability with Virtual Machine Threat Detection (VMTD) in Security Command Center is now generally available for all Google Cloud customers. Follow the link below to read more details: https://cloud.google.com/blog/products/identity-security/introducing-virtual-machine-threat-detection-to-block-critical-threats
Google Cloud Universe is a 3 steps training designed to adapt to your learning stage, from introduction to GCP to advanced deep dive sessions & demos. Topics:Landing Zones, Infrastructure Modernization, Application Development, Data Management & Smart Analytics, and Machine Learning - choose your topic! Follow the link below to join this Google Cloud event for the learning program.https://inthecloud.withgoogle.com/cloud-universe-parent-22/register.html
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/.
Here to bring you the latest news in the Cloud is Sarah Spikes.• New Cloud Regions → https://goo.gle/3QE3uXc• Security Voices → https://goo.gle/3A5y6tD• Data Security → https://goo.gle/3AuRHol• Query Library → https://goo.gle/3QxA5xI Chapters:0:00 - Intro0:14 - New Cloud Regions0:30 - Security Voices0:58 - Data Security1:32 - Query Library1:52 - Wrap up
Google cloud migration should not be complicated! All you need to achieve a successful database migration is knowing the available tools, and the major migration steps.Join the Google cloud event "Database Migration & Modernization" on 6th of September 2022, from 09:00 - 10:00 AM CEST and you will• Understand the benefits of hosting databases in the cloud• Learn how to navigate modernization paths for databases• Find ways to simplify your database migrationsDon’t miss out on this and register here https://cloudonair.withgoogle.com/events/database-migration-modernisation
Recently, Google Cloud Storage launch powerful new object lifecycle rules for GCS to help us control cloud storage costs in new ways. Two new features are included in this launch.Prefix/suffix lifecycle conditionsBefore, we already had several lifecycle conditions to choose from, such as the age of an object, its version history, a custom timestamp, and more. Now, we can add conditions on the names of objects; specifically, matching a prefix or a suffix. Prefix and suffix conditions are helpful for a number of cases. Here are two:Managing common object prefixes separately. It's quite common to group objects using a common prefix, such as in a dataset. Now, lifecycle conditions can act on those groups using a MatchesPrefix rule.Managing categories of objects separately. It's also common to use "extensions" on object keys to denote the format of the data; .mp4, .zip, .csv, and so on. we often have a mix of these within a bucket, and would like to use the extension to manage them separ
Today, Google cloud announced the new Google Cloud SDK/client library reference docs home built by developers for developers. The UI and features are a huge improvement.Check it out:Centralized docs - All Cloud Client Library reference docs can now be found on cloud.google.com. You can see a list of them here: cloud.google.com/sdk Better UI - table of contents, tags, breadcrumbs, highlighting, and descriptions Autogenerated docs & code samples - documentation is automatically updated with each new client library release. Code samples included in docs. Advanced search - search is integrated so results span all pages across the domain. Filters - The filter search box on top of the bar will help you find and choose the reference docs you need. Table of contents - jump to your desired section with just a single click Direct code edits - edit variables directly within code samples and then copy with a click of a buttonFor more information, click the following link as well.https://cloud
Serverless Migration Station is a Serverless Expeditions mini-series, focused on helping developers modernize their applications running on a Google Cloud serverless compute platform. In this video, the second video focused on App Engine Blobstore, Google engineers Martin & Wesley show developers how to migrate an App Engine app using its legacy Python NDB (Datastore) and Blobstore APIs. They also cover the webapp2 framework to Cloud NDB & Cloud Storage, the Flask framework, and upgrading to an app that's both Python 2 & 3 compatible.Click on the video below to view in details: Chapters:0:00 - Introduction0:45 - FOUR migrations?!?2:21 - Preparing for the migrations2:39 - Updating configuration3:59 - Required package updates5:01 - Updating Datastore access5:31 - Updating upload & download handlers6:18 - Updating the main handler6:42 - Validating the migrations7:21 - There's one more thing…8:28 - Summary and references Extra Credit:Codelab → https://goo.gle/3OYjEZS Pytho
Want to learn how to build a three-tier serverless Cloud Run app? In this video show the Serverless three-tier Cloud Run app, Terry Ryan and Martin Omander, developer advocate, at google cloud go through building a three-tier serverless app on Cloud Run! Watch to learn how a serverless three-tier architecture works and how you can get started building your own applications.Click on the video below to watch the full video. Chapters:0:00 - Intro0:30 - Two-tier vs three-tier0:56 - Example web app1:56 - How does three-tier architecture work?4:44 - How can you update the code?5:49 - Wrap up