Google Cloud Platform

Google Cloud Load Balancing enables enterprises and cloud-natives to deliver highly available, scalable, low-latency cloud services with a global footprint.

Overview of connection options → goo.gle/3wlxNZN Cloud SQL provides a managed service for MySQL, PostgreSQL, and SQL Server databases as well as backups, high availability, maintenance, and

Pic-a-Daily Serverless Workshop → goo.gle/3cDzc2U Source code → goo.gle/2QrTBC9 When working with serverless applications that are running on microservices, microservices can

Pub/Sub documentation → goo.gle/3jpURBD Choosing Pub/Sub or Pub/Sub Lite → goo.gle/2UH14Pi In this episode of Pub/Sub Made Easy, Priyanka Vergadia goes into depth about the multiple

Businesses today are realizing that they can use machine learning to improve customer experience.

Google Cloud Platform – 28 апреля 2018, 16:18

In this session, we will show how to use Cloud Speech-to-Text for Human Computer Interaction and Speech Analytics.

Processing payments can be difficult, but with serverless integrations it doesn't have to be.

This video walks you through how to create a virtual private cloud.

Are you an architect or data engineer tasked to explore your GCP data lake? As a CIO, are you thinking about using BigQuery to create a logical data warehouse for your complex organization?

Learn more about Looker → goo.gle/2Y4gb3Z Looking to better understand your enterprises data?

This session presents on how to build serverless workflows orchestrating various products and API calls within Google Cloud and beyond.

Before you can use the power of "the cloud" to serve your content, you have to get your data INTO it.

Use BigQuery to explore the NCAA dataset of basketball games, teams, and players.Try the Exploring NCAA Data with BiqQuery lab here: bit.ly/2rIG6gK *Automatically get 10 free credits to take

This final Keynote is dedicated to developers, focusing on Google’s commitment to open source with talks from Jeff Dean, Senior Google Fellow and leader of the Google Brain team, along with Rajat

There are many ways to use a service account.

Learn more about AppSheet → goo.gle/3oBnVX6 Does your enterprise spend precious time inputting invoices into another data source or disparate system?

In this session, you will learn how to reduce manual effort in deploying and maintaining software on groups of VM instances with help of Managed Instance Groups.

VPC Service Controls enables you to establish security perimeters around sensitive data in Google Cloud Platform services such as Google Cloud Storage and BigQuery.

Learn about Google Cloud security → goo.gle/3gGgCfp Follow Stephanie on Twitter for more cloud content → goo.gle/2Zr0HaO​ What does securing a Google data center really look like?

Recorded on Mar 23 2016 at GCP NEXT 2016 in San Francisco. Scale your applications from zero to full-throttle with Google Cloud Load Balancing - no pre-warming needed.

Hosting your databases in the cloud is easy with Cloud SQL and with a highly available or clustered configuration, you won't have to worry about application downtime!

WordCount Examples → goo.gle/3J7jpsf Google Cloud Dataflow → goo.gle/35KiTmE Beam College → goo.gle/3NdzHDm Welcome back to Getting Started with Apache Beam!

Windows and Triggers notebook → goo.gle/3lqXItT Google Cloud Dataflow → goo.gle/3sKT7XJ Beam College → goo.gle/39CvbyJ How do you handle processing streaming data with

Cloud Dataprep is an intelligent data service for visually exploring, cleaning, and preparing data for analysis.

Object storage is essential to supporting critical enterprise workloads in the cloud—from content serving to data protection to high-performance analytics.

Blog post with code → goo.gle/2yjSE5h In this episode of Serverless Expeditions, Martin Omander and Dustin Ingram show you the dexterity of Python, and how you can easily run jobs on a

TensorFlow is rapidly democratizing machine intelligence.

As you build out your microservice infrastructure, one thing to think about is how you authenticate calls from one service to another.

Neural networks? Deep learning? Deep neural networks? What do these terms mean, and how are they different?