Past Speaker Series
Content and Recordings
Using BigQuery to explore NCAA data + Machine Learning
First half of the game:
BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without managing infrastructure or needing a database administrator. BigQuery uses SQL and takes advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
We have a newly available dataset for NCAA Basketball games, teams, and players. The game data covers play-by-play and box scores back to 2009, as well as final scores back to 1996. Additional data about wins and losses goes back to the 1894-5 season in some teams' cases.
In this hands-on workshop we will find and query the NCAA dataset using BigQuery.
Second half:
Next, we will predict the winner of a NCAA Men's Basketball tournament game using BigQuery, Machine Learning (ML), and the NCAA Men's Basketball dataset.
We will use BigQuery Machine Learning (BQML), which allows you to use SQL to create ML models for classification and forecasting.
You will learn:
- Use BigQuery to access the public NCAA dataset.
- Explore the NCAA dataset to gain familiarity with the schema and scope of the data available.
- Prepare and transform the existing data into features and labels.
- Split the dataset into training and evaluation subsets.
- Use BQML to build a model based on the NCAA tournament dataset.
- Use your newly created model to predict NCAA tournament winners for your bracket.
Ready to create, evaluate, and use machine learning models in BigQuery?
BigQuery Machine Learning (BigQuery ML) enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratise machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement.
There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this lab you will use this data to create a model that predicts whether a visitor will make a transaction.
In our hands-on workshop with Google Engineer Stewart Viera you will learn about the following basic features of BigQuery:
- Query public tables, and load sample data into BigQuery
- Troubleshoot common Syntax errors with the Query Validator
Join us to understand the many powerful learning opportunities Google Cloud has to offer. Explore the breadth of Google Cloud role-based training and certifications. Discover where to get started and how hands-on learning and the earning of Google Cloud credentials and certifications can impact your career.
About the hands-on workshop topics:
Join Cisco and Google Cloud. We explore and learn about:
- Cisco's hiring processes and currently open opportunities
- First-hand experiences from a Cisco new hire and leader
- Google Cloud and Cisco joint partnership and solutions
About the panel discussion:
View a diverse team of career professionals, Googler, and student.
They participate by detailing their experiences in the tech space and answering questions about their career journeys.
Check out the exciting companies we bring you:
- BadgerDAO is a decentralized collective of builders supporting community driven growth for Bitcoin across DeFi.
- Merryfield is a startup company and app delivering the highest standard of "better-for-you" products and clean label food and product choices that do not harm people or the planet.
About the hands-on workshop topics:
Google Cloud Speech API [video]
The Google Cloud Speech API enables easy integration of Google speech recognition technologies into developer applications. The Speech API allows you to send audio and receive a text transcription from the service.
Google Cloud Video Intelligence [video]
Google Cloud Video Intelligence makes videos searchable and discoverable by extracting metadata with an easy to use REST API. You can now search every moment of every video file in your catalog. It quickly annotates videos stored in Cloud Storage, and helps you identify key entities (nouns) within your video; and when they occur within the video. Separate signal from noise by retrieving relevant information within the entire video, shot-by-shot, or per frame.
About the hands-on workshop topics:
Google Cloud Pub/Sub - Qwik Start
Google Cloud Pub/Sub is a messaging service for exchanging event data among applications and services. In this workshop kickoff, you will do the following: Learn the basics of Pub/Sub. Create, delete, and list Pub/Sub topics. Create, delete, and list Pub/Sub subscriptions. Publish messages to a topic. Use a pull subscriber to output individual topic messages. Use a pull subscriber with a flag to output multiple messages.
Vertex AI - Qwik Start
In this workshop section, you will use BigQuery for data processing and exploratory data analysis and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value. The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. You will start with a local BigQuery and TensorFlow workflow that you may already be familiar with and progress toward training and deploying your model in the cloud with Vertex AI.
About the hands-on workshop topics:
Cloud Dataprep by Trifecta [video]
Cloud Dataprep is an intelligent data service for visually exploring, cleaning, and preparing data for analysis. Cloud Dataprep is serverless and works at any scale. There is no infrastructure to deploy or manage. Easy data preparation with clicks and no code! In our workshop you will use Dataprep to manipulate a dataset. You import datasets, correct mismatched data, transform data, and join data. If this is new to you, you'll know what it all is by the end of this lab.
Dataflow: Qwik Start Templates [video]
In this section, you will learn how to create a streaming pipeline using one of Google's Cloud Dataflow templates. More specifically, you will use the Cloud Pub/Sub to BigQuery template, which reads messages written in JSON from a Pub/Sub topic and pushes them to a BigQuery table.
About the hands-on workshop topics:
Cloud Natural Language API [video]
Cloud Natural Language API lets you extract information about people, places, events, (and more) mentioned in text documents, news articles, or blog posts. You can use it to understand sentiment about your product on social media, or parse intent from customer conversations happening in a call center or a messaging app. You can even upload text documents for analysis.
Dataproc: Qwik Start [video]
Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead. Create Cloud Dataproc clusters quickly and resize them at any time, so you don't have to worry about your data pipelines outgrowing your clusters.
About the hands-on workshop topics:
Kubernetes Engine
In this hands-on workshop you will practice with container creation and application deployment with GKE.
Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The Kubernetes Engine environment consists of multiple machines (specifically Compute Engine instances) grouped to form a container cluster.
Set Up Network and HTTP Load Balancers
In this hands-on lab you'll learn the differences between a network load balancer and an HTTP load balancer and how to set them up for your applications running on Compute Engine virtual machines (VMs).
About the hands-on workshop topics:
Creating a Virtual Machine
In this hands-on lab, you'll create virtual machine instances of various machine types using the Google Cloud Console and the gcloud command line. You'll also learn how to connect an NGINX web server to your virtual machine. What you'll do:
- Create a virtual machine with the Cloud Console.
- Create a virtual machine with the gcloud command line.
- Deploy a web server and connect it to a virtual machine.
Getting Started with Cloud Shell and gcloud
In this hands-on lab, you learn how to connect to computing resources hosted on Google Cloud via Cloud Shell with the gcloud tool. Attendees will receive $25 in Google Cloud credits to explore the Cloud Console. Register and receive a direct invite to the Live Google Meet with chat feature.
About the hands-on workshop topics:
Cloud Functions
The Cloud Functions hands-on workshop section shows you how to create, deploy, and test a cloud function using the Google Cloud console or Google Cloud Shell command line.
What you'll do:
- Create a cloud function
- Deploy and test the function
- View logs
Cloud Monitoring
This workshop section will show you how to monitor a Compute Engine virtual machine (VM) instance with Cloud Monitoring. You'll also install monitoring and logging agents for your VM which collects more information from your instance, which could include metrics and logs from 3rd party apps.
In this hands-on workshop you will learn how to:
- Use the Cloud Console to create a storage bucket, then upload objects, create folders and subfolders, and make those objects publicly accessible.
- Assign a role to a second user and remove assigned roles associated with Cloud IAM.
- Experience how granting and revoking permissions works from Google Cloud Project Owner and Viewer roles.
Join Google Developer Student Clubs for a Special Speaker Series as we discuss Diversity, Equity, and Inclusion and the importance of finding your place and space in the workplace.
We are excited to welcome Farah Salam-Hottle to speak and share with Q&A.
Farah is the Senior Director of Diversity, Equity, and Inclusion for Vaco, a global consulting and talent solutions firm with teams across the U.S., Canada, and India. She brings over 15 years in talent acquisition and business development in staffing, consulting, and startups.
Farah’s focus is in developing DEI strategy, programs, and policies at scale. Prior to this, she co-developed the firm’s first Diversity, Equity, and Inclusion council while leading the talent acquisition department.
Farah has developed trainings in cultural literacy, unconscious bias, foundations of DEI, and equitable hiring practices. She sits on the Board of Directors for 501(c)(3) non-profit, Afghan Association of Central Virginia (www.AACVA.us). An active community leader, she is dedicated to creating access and opportunity for underserved communities while uniting cross-cultural teams around a shared vision.
Farah obtained her bachelor’s degree in political science from the Honors College at Virginia Commonwealth University. She has completed post-graduate studies in International DEI and Workplace Diversity. She lives in Hanover, VA with her husband, four children, and a spoiled Maltese named Ziggy.
Vertex Pipelines help you automate and reproduce your ML workflow. Vertex AI integrates the ML offerings across Google Cloud into a seamless development experience. In this hands-on workshop, you will learn how to create and run ML pipelines with Vertex Pipelines.
Why are ML pipelines useful?
Before diving in, first understand why you would want to use a pipeline. Imagine you're building out a ML workflow that includes processing data, training a model, hyperparameter tuning, evaluation, and model deployment. Each of these steps may have different dependencies, which may become unwieldy if you treat the entire workflow as a monolith.
As you begin to scale your ML process, you might want to share your ML workflow with others on your team so they can run it and contribute code. Without a reliable, reproducible process, this can become difficult. With pipelines, each step in your ML process is its own container. This lets you develop steps independently and track the input and output from each step in a reproducible way. You can also schedule or trigger runs of your pipeline based on other events in your Cloud environment, like when new training data is available.
What you'll learn:
- Use the Kubeflow Pipelines SDK to build scalable ML pipelines
- Create and run a 3-step intro pipeline that takes text input
- Create and run a pipeline that trains, evaluates, and deploys an AutoML classification model
- Use pre-built components for interacting with Vertex AI services, provided through the google_cloud_pipeline_components library
- Schedule a pipeline job with Cloud Scheduler
Use Machine Learning to understand the audience of a real mobile application: Flood-It!
Train, tune, evaluate, explain, and generate batch and online predictions with a BigQuery ML XGBoost model. You will use a Google Analytics 4 dataset from a real mobile application, Flood it! (Android app, iOS app), to determine the likelihood of users returning to the application. You will generate batch predictions with your BigQuery ML model as well as export and deploy it to Vertex AI for online predictions using the Vertex Python SDK.
BigQuery ML lets you train and do batch inference with machine learning models in BigQuery using standard SQL queries faster by eliminating the need to move data with fewer lines of code.
Vertex AI is Google Cloud's complimentary next generation, unified platform for machine learning development. By developing and deploying BigQuery ML machine learning solutions on Vertex AI, you can leverage a scalable online prediction service and MLOps tools for model retraining and monitoring to significantly enhance your development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value.
Learn how Google Cloud is showcasing Machine Learning as a future career opportunity.
Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?
AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models. In this hands-on lab, you will learn how to produce a custom ML model that automatically recognizes damaged car parts.
You will interact and request predictions from a hosted model in a different project trained on the same dataset. You will then tweak the values of the data for the prediction request and examine how it changes the resulting prediction from the model.
Cloud Functions is a serverless execution environment for building and connecting cloud services. With Cloud Functions you write simple, single-purpose functions that are attached to events emitted from your cloud infrastructure and services.
Your Cloud Function is triggered when an event being watched is fired. Your code executes in a fully managed environment. There is no need to provision any infrastructure or worry about managing any servers.
Cloud Functions are written in Javascript and executed in a Node.js environment on Google Cloud. You can take your Cloud Function and run it in any standard Node.js runtime which makes both portability and local testing a breeze.
In this workshop we will show you how to create, deploy, and test a cloud function using the Google Cloud console.
Join Googler and author, Sireesha Pulipati, for an introduction to Goggle Studio and its use by Data Analyst.
Data Studio is a free, modern business intelligence product that lets you create dynamic, visually compelling reports and dashboards. With Data Studio, you can:
- Easily connect to a variety of data sources.
- Visualize your data through attractive, dynamic, and interactive reports and dashboards.
Share and collaborate with others, just as you can in Google Drive. Data Studio automatically saves every change you make, so there's no need to click Save when editing a report. In this workshop you will create a report by pulling in a public dataset from BigQuery. You will then add a chart and style to your report, which will make your data elegant and easy to understand.
Join our speaker Kapil Anand for an exploration of Vertex AI.
In the evening's lab we will use BigQuery for data processing and exploratory data analysis and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value.
The site-of-all-sites Google Cloud says this about Vertex AI (and we can't say it better): Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case.
Vertex AI is Google Cloud's next generation, unified platform for machine learning development and the successor to AI Platform announced at Google I/O in May 2021. By developing machine learning solutions on Vertex AI, you can leverage the latest ML pre-built components and AutoML to significantly enhance development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value.
Join our speaker Will Lifferth for an introduction to BigQuery.
Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
You can access BigQuery in the Console, the Web UI or a command-line tool, or by making calls to the BigQuery REST API using a variety of client libraries such as Java, .NET, or Python. There are also a variety of third-party tools that you can use to interact with BigQuery, such as visualizing the data or loading the data. This hands-on lab shows you how to use the Web UI to query public tables and load sample data into BigQuery.
Content presentation deck here.
What is ETL? ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.
ETL can be used to store legacy data, or—as is more typical today—aggregate data to analyze and drive business decisions. In this Speaker Series event you build several Data Pipelines that ingest data from a publicly available dataset into BigQuery, using these Google Cloud services:
- Cloud Storage
- Dataflow
- BigQuery
You will create your own Data Pipeline, including the design considerations, as well as implementation details, to ensure that your prototype meets the requirements.
Join us as we Implement an AI Chatbot for Google Assistant with Dialogflow ES (lab link).
Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, etc. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers either through text or with synthetic speech.
In this lab, you will build a Google Assistant chatbot that submits helpdesk tickets.
Join us as we introduce you to Google Cloud's App Engine.
Introduction to App Engine
The App Engine standard environment makes it easy to build and deploy an application that runs reliably even under heavy load and with large amounts of data. It includes the following features:
- Persistent storage with queries, sorting, and transactions.
- Automatic scaling and load balancing.
- Asynchronous task queues for performing work outside the scope of a request.
- Scheduled tasks for triggering events at specified times or regular intervals.
- Integration with other Google cloud services and APIs.
Deploy a website using App Engine with this quickstart.
Join us as we introduce you to Google Cloud APIs and Cloud Vision API.
Introduction to Cloud APIs
APIs (Application Programming Interfaces) are software programs that give developers access to computing resources and data. Companies from many different fields offer publicly available APIs so that developers can integrate specialized tools, services, or libraries with their own applications and codebase.
We will share the architecture and basic functioning of APIs. This will be supplemented with hands-on practice, where you will configure and run Cloud Storage API methods in Google Cloud Shell. At the end of the evening you will understand key principles of API communication, architecture, and authentication. You will also gain practical experience with APIs, which you can apply to future labs or projects.
Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API
To complete our understanding we will explore the Cloud Vision API. We will send images to the Vision API and see it detect objects, faces, and landmarks.
Join us as we prepare you for the hackathon season.
In this session we will explore the Google Cloud console with credits for every participant. Our Developer Advocates will then guide you through Google Cloud's Hackathon Toolkit and prepare you to be a successful participant in the fun season ahead.
Explore Cloud APIs & Kubernetes Engine in this workshop guided by a Google Customers Solution Engineer - Hema Patil.
What are Google Cloud APIs? Cloud APIs allow you to automate your workflows by using your favorite language. Use these Cloud APIs with REST calls or client libraries in popular programming languages.
What is Google Kubernetes Engine (GKE)? Google Kubernetes Engine (GKE) provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The Kubernetes Engine environment consists of multiple machines (specifically Compute Engine instances) grouped to form a container cluster.
Join Sanmay Mishra, Infrastructure Cloud Consultant, for a hands-on workshop introducing you to Dataplex and Dataprep.
Break free from data silos with Dataplex’s intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale.
Get introduced to Dataplex via YouTube here and review a Dataprep workshop preview here.
Join Kunjan Patel, Software Engineer, for a hands-on workshop introducing you to the concepts of Pub/Sub and creating a streaming pipeline using Dataflow templates.
Join Tushar Gupta, Customer Engineer-Manufacturing, for a hands-on workshop introducing you to the concepts of creating a Virtual Machine on Google Cloud and an introduction to Cloud Identity and Access Management (IAM).
Join Trina L. Martin, technologist, speaker, and author, as she discusses her career journey. Register and you will receive a direct invite to the Live Google Meet with chat feature.
About Trina
Trina L. Martin is a technologist, international speaker, best-selling author, and podcaster. She uses her experiences to motivate audiences to overcome adversity, develop self-determination, and discipline. Trina also inspires emerging leaders to pursue their wildest dreams with heart and grit. An accomplished IT professional and retired U.S. Naval Officer with 30 years of service, she has broken barriers and made strides in her career that many said weren’t possible.
A native of Chicago, IL, Trina is the youngest of four children. The first in her family to attend college, and due to the lack of support at home, she knew she would have to achieve her own goal. With unwavering determination, she enlisted in the U.S. Army and served in the reserves to put herself through school at Alabama A&M University. Both of these choices fueled Trina’s desire to succeed and revealed the depths of her resiliency.
Naturally skilled at finding the harmony between technology, science, and data, she has led a stellar career in the Information Technology field. As an Intelligence Officer in the U.S. Navy, a Cyber Intelligence Analyst for the FBI, and now as an advocate for WOC in tech, Trina has pierced the glass ceiling and excelled in positions typically held by men.
Throughout Trina’s career, the common denominator has been her ability to set goals and not only achieve but surpass them. Self-motivation, discipline, and hard work are the driving forces that helped her design a life and career that defied the odds stacked against her.
Join us this coming Thursday, July 21st at 7pm ET for a casual chat with a Google Developer Student Club Lead René Capella.
René is a human-centered design and engineering student at the University of Washington minoring in Ethics. She is a participant this summer in Design for Passion, a HCI researcher with KidsTeam, a Woman Techmaker Ambassador, Google DSC founder at North Seattle college, GDSC president University of Washington, and Microsoft Student Ambassador. Connect with René on LinkedIn where she loves (LOVES) to discuss mentorship in tech!
Join a recording of our casual chat with a Google Developer Advocacy intern Kelci Mensah. Visit Kelci’s LinkedIn page here and you will find her pathway to joining Google.
Join Google Cloud Developer Advocate (DA) Ryan Matsumoto this Thursday. He will be detailing his experiences as a DA and answering questions about his career journey. We are excited for you to interact with Ryan and look forward to seeing you Thursday.
To see the type of content Ryan produces as a DA and gain an understanding of his role before our interactive chat, check out this video.
Part 2 of Build and Deploy Machine Learning Solutions on Vertex AI
Join Google Cloud Engineers where you will learn how to: Use Google Cloud’s unified Vertex AI platform and its AutoML. Create custom training services to train, evaluate, tune, explain, and deploy machine learning solutions. These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.
Part 1 of Build and Deploy Machine Learning Solutions on Vertex AI
Join a Google Cloud Engineer and learn how to use Google Cloud’s unified Vertex AI platform and its AutoML and custom training services to train, evaluate, tune, explain, and deploy machine learning solutions.
These labs and their lab series are for those interested in becoming a professional Data Scientists and Machine Learning Engineer. The datasets and labs are built around high business impact enterprise machine learning use cases; these include retail customer lifetime value prediction, mobile game churn prediction, visual car part defection identification, and fine tuning BERT for review sentiment classification. Learners who complete this skill badge will gain hands-on experience with Vertex AI for new and existing ML workloads and be able to leverage AutoML, custom training, and new MLOps services to significantly enhance development productivity and accelerate time to value.
These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.
Join Google Cloud Engineers and learn how to do the following using Explainable AI:
- Build and deploy a model to an AI platform for serving (prediction)
- Use the What-If Tool with an image recognition model
- Identify bias in mortgage data using the What-If Tool
- Compare models using the What-If Tool to identify potential bias
These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.
Join Google Developer Relations Engineer Paul Ruiz for a hands-on workshop showcasing his experiences with Google Cloud as he guides you in learning the basic features for the following machine learning and AI technologies:
- BigQuery
- Cloud Speech AI
- Cloud Natural Language API
- AI Platform, Dataflow
- Cloud Dataprep by Trifacta
- Dataproc
- Video Intelligence API
These labs are great resources for understanding topics that appear in the Machine Learning Engineer learning pathway.
Join Google Cloud Engineers, Sonakshi and Thomas, for hands-on workshops showcasing how to:
- Build data pipelines using Cloud Dataprep by Trifacta, Pub/Sub, and Dataflow.
- Use Cloud IoT Core to collect and manage MQTT-based devices.
- Use Cloud Storage, Dataflow, and BigQuery to perform ETL.
- Build a machine learning model using BigQuery ML.
- Use Cloud Composer to copy data across multiple locations.
These labs are great resources for understanding topics that will appear in the Google Cloud Certified Professional Data Engineer Certification.
Join a Google Software Engineers Aarti and Kevin for a hands-on workshop showcasing how to Create ML Models with BigQuery ML.
BigQuery Machine Learning (BQML, product in beta) enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratise machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement. There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this workshop you will use this data to create a model that predicts whether a visitor will make a transaction.
These labs are great resources for understanding topics that will appear in the Google Cloud Certified Professional Data Engineer Certification.
Join Cara and Praneeth for hands-on workshops where you will learn about the following tools and techniques to help optimize resource usage and eliminate unnecessary costs on Google Kubernetes Engine (GKE):
- create and manage a multi tenant cluster
- monitor resource usage by namespace
- configure cluster and pod autoscaling
- configure load balancing
- set up liveness and readiness probes
Once you complete these labs, check out the Cloud Architect learning path preparing you for the Google Cloud Professional Cloud Architect certification to take the next steps in your professional journey.
Join Silvia for a hands-on workshop and learn how to:
- Deploy and manage a containerized application using Google Kubernetes Engine.
- Launch a VM-based application using Cloud Deployment Manager, and then monitor and stress the application.
- Deploy continuous delivery pipelines using Google Kubernetes Engine and Spinnaker.
- Create and control access to multiple VPC networks.
- Configure and use Cloud Monitoring to troubleshoot breaks in applications.
Once you complete these labs, check out the Cloud Architect learning path preparing you for the Google Cloud Professional Cloud Architect certification to take the next steps in your professional journey.
Part 1: Deploy and Manage Cloud Environments with Google Cloud
Join Abel for hands-on workshops and learn how to:
- Deploy and manage a containerized application using Google Kubernetes Engine and kubectl.
- Launch a VM-based application using Cloud Deployment Manager, and then monitor and stress the application.
- Deploy continuous delivery pipelines using Google Kubernetes Engine and Spinnaker.
- Create and control access to multiple VPC networks.
- Configure and use Cloud Monitoring to troubleshoot breaks in applications.
Once you complete these labs, check out the Cloud Architect learning path preparing you for the Google Cloud Professional Cloud Architect certification to take the next steps in your professional journey.
Automating Infrastructure with Terraform
Experience a hands-on workshop and learn how to write infrastructure as code with Terraform. In this workshop, you will get hands-on experience building, changing, and destroying infrastructure, managing local and remote state, importing infrastructure, and building your own modules.
See our speaker Stewart's LinkedIn profile here.
Join Google Software Engineer Aarti Panda for a hands-on workshop and learn multiple ways to deploy and monitor applications, including how to:
- Explore IAM roles and add/remove project access
- Create VPC networks
- Deploy and monitor Compute Engine VMs
- Write SQL queries
- Deploy and monitor VMs in Compute Engine
- Deploy applications using Kubernetes with multiple deployment approaches
Join Google Developer Programs Engineer Laurie White for an evening with SQL (Structured Query Language).
SQL is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis.
Laurie will introduce you to SQL and prepare you for the many labs and quests in Google Cloud Skills Boost on data science topics.
Join Google Developer Advocate Ryan Matsumoto for a hands-on workshop and learn more about using Google Cloud to predict the winner of a NCAA Men's Basketball tournament game using BigQuery, Machine Learning (ML), and the NCAA Men's Basketball dataset.
In the workshop you will use BigQuery Machine Learning (BQML), which allows you to use SQL to create ML models for classification and forecasting. You will learn how to:
- Use BigQuery to access the public NCAA dataset.
- Explore the NCAA dataset to gain familiarity with the schema and scope of the data available.
- Prepare and transform the existing data into features and labels.
- Split the dataset into training and evaluation subsets.
- Use BQML to build a model based on the NCAA tournament dataset.
- Use your newly created model to predict NCAA tournament winners for your bracket.
Join Google Software Engineer Rayan Dasoriya for a hands-on workshop and learn more about using Google Cloud to build your dynamic website, including how to:
- Deploy a website on Cloud Run.
- Host a web app on Compute Engine.
- Create, deploy, and scale your website on Google Kubernetes Engine.
- Migrate from a monolithic application to a microservices architecture.
Join Google Software Engineer Jennie Brown for a hands-on workshop showcasing how to:
- Write gcloud commands and use Cloud Shell
- Create and deploy virtual machines in Compute Engine
- Run containerized applications on Google Kubernetes Engine
- Configure network and HTTP load balancers
Join Google Software Engineer Monique (Mezher) de Zahr for a hands-on workshop showcasing her experiences with Google Cloud as she guides you in learning the basic features for the following machine learning and AI technologies:
- BigQuery
- Cloud Speech AI
- Cloud Natural Language API
- AI Platform, Dataflow
- Cloud Dataprep by Trifacta
- Dataproc
- Video Intelligence API
Join Google Customer Engineer Sagar Kewalramani for a hands-on workshop showcasing how to Create ML Models with BigQuery ML.
BigQuery Machine Learning (BQML, product in beta) enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratise machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement. There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this workshop you will use this data to create a model that predicts whether a visitor will make a transaction.
Join Google Cloud Developer Programs Engineer Charlie Engelke and learn how to use Cloud Functions. You will:
- Create a small serverless application demonstrating a simple microservices architecture
- Create a Pub/Sub topic and subscription
- Create Cloud Functions to process published messages in JavaScript and Python, using the Cloud Console UI, and using gcloud command line interface in Google Cloud Shell.
- Create a CloudSQL database to store the status of the processed widgets.
- Use the Runtime Configurator to store and share credentials with Cloud Functions.
- Create a Cloud Scheduler Job to call an HTTP end point.
- How to filter and export Cloud Logging messages to Cloud Pub/Sub
- How to trigger Cloud Functions from Pub/Sub
- How to do write a Cloud Function to do simple processing
- How to update a VM's metadata
Join Google Cloud Engineering Manager, Cloud AI/ML, Kapil Anand and learn how to do the following using Explainable AI:
- Build and deploy a model to an AI platform for serving (prediction)
- Use the What-If Tool with an image recognition model
- Identify bias in mortgage data using the What-If Tool
- Compare models using the What-If Tool to identify potential bias
Join Google Cloud Customer Engineer Sayantani Goswami to begin the Google Cloud credentialed labs series - Build and Secure Networks in Google Cloud. By joining the event you will receive a free month of access to a library of labs you can complete during these Thursday events or on your own.
In Sayantani's hands-on workshop you will learn about multiple networking-related resources to build, scale, and secure your applications on Google Cloud, including how to:
- Enable Identity-Aware Proxy.
- Create VPC networks.
- Create virtual machine instances with nginx web servers using Compute Engine.
- Create firewall rules to control internal and external access to your VMs.
- Configure, stress, and protect a multi-region HTTP application with an HTTP load balancer and Google Cloud Armor.
- Configure and test an internal TCP load balancer with a regional backend service.
Join Google Cloud Developer Advocate Ryan Matsumoto and learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies:
- Cloud Storage
- Identity and Access Management
- Cloud Functions
- Pub/Sub
Announcing a special launch for higher education students providing you with the opportunity to join an exclusive and invite-only round-table of students providing feedback of this new certification experience.
Participants who complete the experience will receive a no-cost opportunity to complete the Cloud Digital Leader certification.
What is a Cloud Digital Leader?
A Cloud Digital Leader can articulate the capabilities of Google Cloud core products and services and how they benefit organizations. The Cloud Digital Leader can also describe common business use cases and how cloud solutions support an enterprise.
The Cloud Digital Leader exam is job-role agnostic and does not require hands-on experience with Google Cloud.
The Cloud Digital Leader exam assesses your knowledge in three areas:
- General cloud knowledge
- General Google Cloud knowledge
- Google Cloud products and services
Join a Google Engineer for a hands-on workshop experiencing how to:
- Recognize and assign roles and users using Identity and Access Management (IAM).
- Assign predefined roles and create custom roles.
- Create and manage service accounts. Securely enable private connectivity between resources in multiple virtual private clouds (VPCs).
- Restrict application access based on authentication using Identity-Aware Proxy.
- Set up a secure Cloud Storage bucket and view related audit logs.
- Manage keys and encrypted data using Key Management Service.
- Create a private Kubernetes cluster where nodes are not publicly accessible.
Join Google Cloud Infrastructure Engineer Darpan Shah and learn how to build and connect cloud infrastructure using the basic capabilities of the of the following technologies:
- AI Platform
- BigQuery
- Cloud Speech API
- Cloud Natural Language API
- DataFlow
- Dataprep
- Video Intelligence
Join Google Cloud Software Engineer Pedro Silva and learn more about the many fundamental features of cloud security, including how to:
- Recognize and assign roles and users using Identity and Access Management (IAM).
- Assign predefined roles and create custom roles.
- Create and manage service accounts.
- Securely enable private connectivity between resources in multiple virtual private clouds (VPCs).
- Restrict application access based on authentication using Identity-Aware Proxy.
- Set up a secure Cloud Storage bucket and view related audit logs.
- Manage keys and encrypted data using Key Management Service.
- Create a private Kubernetes cluster where nodes are not publicly accessible.
Join a Google Engineer for a hands-on workshop showcasing how Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically.
This workshop will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. RSVP for the workshop today.
Part 1 - Flutter Development
Explore the fundamentals of Flutter application development in Part 1 of the workshop event! During the workshop you will build a "Hello World" Flutter application, design a frontend for a shopping application, and learn how to connect your Flutter applications to backend services. You will utilize a pre-provisioned development environment allowing minimal setup to get into the application code.
Part 2 - Flutter Startup Namer
Implement a simple app that generates proposed names for a startup company. You'll learn:
- How to write a Flutter app that looks natural on iOS, Android, and the web
- Basic structure of a Flutter app
- Finding and using packages to extend functionality
- Using hot reload for a quicker development cycle
- How to implement a stateful widget
- How to create an infinite, lazily loaded list
Join Google Cloud Customer Engineer, Stenio Ferreira, for a hands-on workshop experiencing:
- Importing Data to a Firestore Database - upload existing data (a CSV file) to a Firestore serverless database in the cloud.
- Build a Serverless Web App with Firebase - create a serverless web app with Firebase, which allows users to upload information and make appointments with the fictional Pet Theory clinic.
- Deploy a Hugo Website with Cloud Build and Firebase Pipeline - learn how to deploy a static Hugo based website using Cloud Build and Firebase.
- Google Assistant: Build an Application with Dialogflow and Cloud Functions - build a Google Assistant application with Dialogflow and Cloud Functions for Firebase.
Join Google Software Engineer Andrea Wu and learn how you can leverage Firebase in your class and hackathon projects.
Firebase is a backend-as-service (Bass) platform for creating mobile and web applications. In this quest you will learn to build serverless web apps, import data into a serverless database, and build a Google Assistant application with Firebase and its Google Cloud integrations.
Join Google Cloud Software Engineer Rayan Dasoriya for a hands-on workshop where you will use Cloud Run to learn how to:
- Use Cloud Run to connect and leverage data stored in Cloud Storage.
- Build a resilient, asynchronous system with Cloud Run and Pub/Sub.
- Build a REST API gateway using Cloud Run.
- Build and expose service using Cloud Run.
Join one of our most popular workshop facilitators, Google Cloud Developer Programs Engineer Laurie White, for a hands-on workshop where you will learn how to build Google Assistant apps, including how to:
- Create an Actions project.
- Integrate Dialogflow with an Actions project.
- Test your application with Actions simulator.
- Build an Assistant application with flash cards template.
- Integrate customer MP3 files with your Assistant application.
- Add Cloud Translation API to your Assistant application.
- Use APIs and integrate them into your applications.
Join Google Cloud Customer Engineer Sireesha Pulipati for a hands-on workshop using BigQuery.
Projects and solutions are built upon data. Who doesn't love data? And analyzing data effectively and efficiently is step one in solving the world's needs in your next project. Prepare for the hackathon season and join a Google Cloud Engineer for a hands-on workshop using BigQuery.
Learn about the following basic features of BigQuery:
- Write SQL queries
- Create and manage database tables in Cloud SQL
- Query public tables, and load sample data into BigQuery
- Troubleshoot common Syntax errors with the Query Validator
- Use Google Apps Script
- Create a chart in Google Sheets, and export that data to Google Slides
- Create reports in Google Data Studio by connecting to BigQuery data
Join Google Cloud Engineer Thomas Munduchira for a hands-on workshop using Google App Engine.
App Engine applications automatically scale based on incoming traffic. load balancing, microservices, authorization, SQL and NoSQL databases, Memcache, traffic splitting, logging, search, versioning, roll out and roll backs, and security scanning are all supported natively and are highly customizable.
App Engine's environments, the Standard environment and the Flexible environment , support a host of programming languages, including Java, Python, PHP, Node.js, Go, etc.. The two environments give users maximum flexibility in how their application behaves since each environment has certain strengths. Read The App Engine Environments for more information.
This workshop uses the sample code from the Google Cloud Node.js Getting Started Guide
You will learn how to:
- Connect to computing resources hosted on Google Cloud via the web.
- Use Cloud Shell and the Cloud SDK gcloud command.
- How to create a Node.js Express application on Google App Engine.
- How to update the code without taking the server down.
Prepare for the hackathon season and join Google Cloud Developer Advocate Ryan Matsumoto for a hands-on workshop. Google Cloud's industry partner Block.one will be joining to introduce you to their company and how they utilize continuing education to stay up to date on the newest Google Cloud technologies.
Blockchain and related technologies such as distributed ledger and distributed apps are becoming new value drivers and solution priorities in many industries. In this workshop you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud.
- Become aware of the cryptocurrency datasets updated in real-time in BigQuery
- Use data visualization tools to map datasets and transaction flows
- Explore interesting transaction questions and answers with advanced SQL queries
- Launch your own blockchain distributed ledger with the open source Hyperledger in GCP
Prepare for the hackathon season and join Google Cloud Customer Engineer Veda Shridharan for a hands-on workshop.
Learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.
Prepare for the hackathon season and join Google Software Engineer Gonzalo Gasca Meza for a hands-on workshop exploring cryptocurrency with BQML.
- Dig into the fates of the bitcoin transactions tied to the infamous 10,000 bitcoin pizza purchase. You will visualize the bitcoin pizza transaction using BigQuery and AI Notebooks.
- Track cryptocurrency exchange trades with Google Cloud in real-time. We’ll walk through how to set up and configure a pipeline for ingesting real-time, time-series data from various financial exchanges and how to design a suitable data model, which facilitates querying and graphing at scale.
Wouldn’t it be awesome to have an accurate estimate of how long it will take for tech support to resolve your issue? Join us on August 5th and build a simple machine learning model for predicting helpdesk response time using BigQuery Machine Learning. You will then build a simple chatbot using Dialogflow, and learn how to integrate your trained BigQuery ML model with your helpdesk chatbot. The final solution will provide an estimate of response time to users at the moment a request is generated.
Prepare for the hackathon season and join Google Customer Engineer Kanchana Patlolla for a hands-on workshop showcasing how to Create ML Models with BigQuery ML. You learn how to use BigQuery ML to:
- Create machine learning models
- Create a classification model
- Create a forecasting model
- Implement a chatbot using Dialogflow for dynamic real-time responses
Prepare for the hackathon season and join Google Customer Engineer Sagar Kewalramani for a hands-on workshop showcasing how to Create ML Models with BigQuery ML.
BigQuery Machine Learning (BQML, product in beta) enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratise machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement. There is a newly available ecommerce dataset that has millions of Google Analytics records for the Google Merchandise Store loaded into BigQuery. In this workshop you will use this data to create a model that predicts whether a visitor will make a transaction.
Join Google Software Engineer Andrea Wu and learn how you can leverage Firebase in your class and hackathon projects.
Firebase is a backend-as-service (Bass) platform for creating mobile and web applications. In this quest you will learn to build serverless web apps, import data into a serverless database, and build a Google Assistant application with Firebase and its Google Cloud integrations.
Join Google Cloud Engineering Manager, Cloud AI/ML, Karl Weinmeister and learn how to do the following using Explainable AI:
- Build and deploy a model to an AI platform for serving (prediction)
- Use the What-If Tool with an image recognition model
- Identify bias in mortgage data using the What-If Tool
- Compare models using the What-If Tool to identify potential bias
Join Google Cloud Engineer Kapil Anand for a hands-on workshop where you learn how to:
- Write gcloud commands and use Cloud Shell
- Create and deploy virtual machines in Compute Engine
- Run containerized applications on Google Kubernetes Engine
- Configure network and HTTP load balancers
Join Google Cloud Software Engineer Haniel Diaz and learn the basic features for the following machine learning and AI technologies:
- Cloud Vision API
- Cloud Translation API
- Cloud Natural Language API
Join Google Cloud Developer Programs Engineer Laurie White and learn the basic features for the following machine learning and AI technologies:
- BigQuery
- Cloud Speech AI
- Cloud Natural Language API
- AI Platform
- Dataflow
- Cloud Dataprep by Trifacta
- Dataproc
- Video Intelligence API
Join Google Cloud Developer Advocate Ryan Matsumoto and learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the of the following technologies:
- Cloud Storage
- Identity and Access Management
- Cloud Functions
- Pub/Sub
Join Google Developer Ecosystems Program Manager Kyle Paul and learn how to build Google Assistant applications, including how to:
- Create an Actions project.
- Integrate Dialogflow with an Actions project.
- Test your application with Actions simulator.
- Build an Assistant application with flash cards template.
- Integrate customer MP3 files with your Assistant application.
- Add Cloud Translation API to your Assistant application.
- Use APIs and integrate them into your applications.
Join Google Cloud Technical Solutions Engineer Sanjog Sharma and learn more about using Google Cloud to build your dynamic website, including how to:
- Deploy a website on Cloud Run.
- Host a web app on Compute Engine.
- Create, deploy, and scale your website on Google Kubernetes Engine.
- Migrate from a monolithic application to a microservices architecture.
Join Google Cloud Engineer Laurie White and learn more about the many features of BigQuery, including how to:
- Write SQL queries
- Create and manage database tables in Cloud SQL
- Query public tables
- Load sample data into BigQuery
- Troubleshoot common Syntax errors with the Query Validator
- Use Google Apps Script
- Create a chart in Google Sheets
- Export that data to Google Slides
- Create reports in Google Data Studio by connecting to BigQuery data
Join Google Cloud Software Engineer Pedro Silva and learn more about the many fundamental features of cloud security, including how to:
- Recognize and assign roles and users using Identity and Access Management (IAM).
- Assign predefined roles and create custom roles.
- Create and manage service accounts.
- Securely enable private connectivity between resources in multiple virtual private clouds (VPCs).
- Restrict application access based on authentication using Identity-Aware Proxy.
- Set up a secure Cloud Storage bucket and view related audit logs.
- Manage keys and encrypted data using Key Management Service.
- Create a private Kubernetes cluster where nodes are not publicly accessible.