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withVR Uses the Power of VR to Prep People with Speech Disorders for Real-life Speaking Situations
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Editor’s note: Meet Gareth Walkom, an entrepreneur dedicated to helping others with speech disorders.
Turning life experience into innovation
Did you know that 3% of Americans have a speech disorder, while 1% of the world’s population have a stutter? Just getting what they need in everyday interactions can be stressful, which intensifies when the stakes are raised during job interviews, presentations, public speaking, and other activities. As a result, some people with speech disorders may avoid conversations and relationships, and risk being denied jobs because of a difference in how they speak.
As a person who stutters, I know firsthand the ableism that people with a speech disorder can encounter in wanting to use their voice in a judgemental world: the frustration of sometimes not being able to say exactly what you want to say and therefore speaking less in speaking situations. And the educational and career opportunities are lost when doors remain closed to us, especially when employers advertise their jobs as requiring someone who speaks the language ‘fluently’.
While researchers still don’t definitively know what causes stuttering, emerging technologies are giving us new and promising pathways for improving the quality of life of people with speech disorders.
That’s why after years of researching and testing potential therapeutic uses of virtual reality, and with the support of the Google for Startups Cloud Program, I founded withVR on International Stuttering Awareness Day (October 22) in 2020. The mission of withVR is to prepare people with speech disorders for real-life speaking situations by utilizing the power of virtual reality.
Working through it
One of the difficulties in adapting to any disability is the opportunity to work through it in a safe and nonjudgmental environment. withVR provides a virtual space for individuals, in collaboration with their speech therapists, to practice real-world speaking scenarios in safe, controlled environments.
Imagine being able to raise your hand in class and give your opinion without hesitation, ordering the meal you want rather than something that’s easier to say, or sit across the table from an avatar of an employer and explain why you are the right person for your dream job. Then further customize the speaking situation and its surroundings to challenge yourself and be ready for anything. That’s what withVR offers individuals and their speech therapists.
Making VR come to life
To bring the withVR vision to life, we are developing applications using the Unity game engine on Google Cloud with integrated Firebase services including authentication, web hosting, storage, and database. It’s a powerful combination that’s enabled us to build industrial-strength applications that we’ve rapidly deployed on a global basis. Today we are already collaborating with 80+ labs, clinics, and hospitals in more than 20 different countries worldwide.
These organizations help us to test and refine a virtual reality application to support people in achieving their speech goals and build comfort through immersive VR experiences using easily available viewers like Google Cardboard.
The application works in conjunction with a web app through which speech therapists configure customized VR scenarios for their patients to use. As no real-life speaking situation is ever exactly the same, customization of VR scenarios is vital. They can construct different scenes, create and script avatars, and through the Google Text-to-Speech API can even choose from hundreds of different voices in a variety of languages. This gives them the flexibility to create many unique speaking situations for their clients no matter where they are in the world.
Progress from the practice sessions is presented through a dashboard that provides therapists with a tool to monitor their clients’ progress and provide feedback and encouragement.
No shortage of support
My founder’s journey has been supported by many passionate people. The Google for Startups Cloud Program has been instrumental in helping us come so far in the first year, and we’ve only scratched the surface of what’s possible. There are many capabilities in Firebase and Google Cloud that we have yet to explore, and through the startup program I now have a Google Mentor who can help guide that exploration.
We also joined the 2Gether-International (2GI) Tech Cohort, which is supported by Google for Startups and is built for and run by entrepreneurs with disabilities. At the end of the 10-week cohort, we finished with a pitch competition, where I was one of six selected founders to pitch in just three minutes. I was very fortunate to win the Best Overall Pitch Award, gaining 10,000 USD in seed funding. This award not only highlights the potential of withVR, but also showcases that anyone can pitch their idea in a short amount of time no matter their difference.
Working with 2GI also gave me the opportunity to collaborate and learn from other founders who have disabilities. It’s a safe space where I don’t have to explain my everyday challenges and can focus on the all-important task of advancing the vision of withVR, while seeing how others use technology in their domain.
Building on a strong foundation
I’m amazed to look back and see what we’ve accomplished in just one year and humbled by the thousands of lives we’ve touched. Every day we receive valuable real-life feedback from people in the field—both clinicians and those with speech differences who benefit from VR therapy. That knowledge tells us that we are heading in the right direction and opening our eyes to new possibilities for where to take withVR. And inspiring us to keep moving ahead.
If you’d like to participate in testing or if you are speech therapist or researcher, please feel free to reach out to us. We’d love to show you how you can contribute to a world where anyone with a speech disorder can truly use their voice in any situation. If you’d like to take part, contact us at hello@withvr.app.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Cloud FinOps Breaks Down Gaps in Finance, Tech and Business Teams, Accelerating Digital Transformation!
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Accelerating digital transformation
Digital transformation is what propels businesses and industries forward. Organizations of all sizes—from startups to global enterprises—focus on digital transformation not only to make scaled improvements, but also to drive significant change and fully embrace the digital age. The pandemic has jump started and pushed many organizations into full gear to digitize their business models and transform with increased business agility, resiliency, and velocity, while driving new innovation and business values for the customers.
However, according to the Boston Consulting Group, only about 30% of companies navigate a digital transformation successfully. Many large scale digital transformation programs failed because of lack of clear business priorities, top-down executive sponsorship, or dedicated resources and commitment to see it through.
Laying the foundation for digital transformation success
Digital transformation drives foundational change in how an organization operates, optimizes internal resources, and delivers value to customers; however, this doesn’t just happen overnight. Digital transformation requires a programmatic approach through an incremental yet agile, cost-effective, value-driven, and sustainable strategy to drive successful transformation across the organization.
One of the critical factors foundational to success is Cloud FinOps (Cloud Financial Operations). Cloud FinOps is an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and accelerate business value realization through cloud transformation. In the context of Digital Transformation, it requires new ways of working and operating models to drive behaviors and cultural change that foster cross-functional collaboration, drive accountability, provide greater cost transparency, and promote a blameless culture.
Most importantly, Cloud FinOps serves as an enabling function to drive successful digital transformation programs and enable business agility by breaking down the boundaries between technology, finance, and business teams. Through this cross-functional team collaboration, technology leaders partner with finance and business leaders to better understand the technology investments to create sustainable business outcomes. By doing so, business priorities become more clear and the focus shifts to value creation, customer-centricity, and innovation.
As such, companies are reinventing their business models to fund value streams and connect cloud technology investments to strategic business outcomes. With the increased visibility of the cloud costs, finance teams are also gaining greater accuracy in tracking cloud spend against budgets. Organizations can align the TCO of the technology services to the value metrics to make better informed future investment decisions and forecast demand.
Cloud FinOps to accelerate business value realization
The successful deployment and implementation of Cloud FinOps building blocks will enable organizations to accelerate digital transformation beyond cost savings, including the ability to:
- Accelerate business value realization and innovation
- Drive financial accountability and visibility
- Optimize cloud usage and cost efficiency
- Enable cross organizational trust and collaboration
- Prevent cloud spend sprawl
- Break down of departmental silos
Organizations that are successful in digital transformation most often have established processes to measure and track business value. One of the key building blocks of Cloud FinOps is “Measurement & Realization.” By establishing a robust value measurement approach to track and monitor the business value metrics toward business goals, we are bringing technology, finance, and business leaders together through the discipline of Cloud FinOps to show how digital transformation is enabling the organization to create new innovative capabilities and generate top-line revenue.
Business value metrics fall across several factors: cost efficiency, resiliency, velocity, innovation, and sustainability. We suggest assigning KPIs to the following metric categories:
Cost efficiency: Measure cost efficiency through infrastructure savings, migration, and support costs. Customers will commonly start with metrics such as cost of compute and storage per day-week-month, and evolve to unit metrics such as cost per customer served or cost per transaction, where the cost of an application stack is aligned to customer drivers.
Resiliency: Enhance operational resiliency with improvement in service quality and security risk posture. Traditional measures such as system service level and the frequency and duration of critical downtime events are effective measures of IT durability. Customers can also augment these metrics by associating a cost per minute of downtime events, reflecting not only the direct impact of these events but opportunity costs as well.
Velocity: Decrease time to market by accelerating fluidity in product and service delivery. By moving to a cloud-based microservices architecture, customers commonly achieve benefits of increasing software release frequency, as well as being able to run many more test scenarios prior to release, resulting in higher quality code. As an example, our recent Google’s State of DevOps Report 2021, shows that elite performers have 973x more frequent code deployment and release frequency than the low performers.
Innovation: Enable a culture of rapid experimentation to drive innovation and cloud transformation. With cloud technology, companies can avoid the financial constraints of fixed cost investments and lengthy procurement lead times. As a result, the marginal cost of experimentation and time from ideation to experimentation can drop significantly while the number of experiments per unit of time can grow dramatically.
Sustainability: Embed true environmental and social sustainability metrics across the organization by adopting a circular economy strategy and building sustainability into everything we do – from running applications on zero net emissions virtual machines to reducing carbon footprint with enhanced productivity and collaboration services. According to Accenture, companies with average on-premise to cloud migrations can drive 65% energy reduction and carbon emission reduction of 84%1.
Getting started
The Cloud FinOps journey starts with defining or updating your metrics. Since business goals and strategic imperatives will likely change over time, it is important to review the Cloud FinOps metrics whenever the goals change. The review of metrics should include the changes in business goals when there are changes in the internal priorities of the team. Executive leaders need to identify dependency relationships between technology and business outcomes to improve the impact of metrics on decision making and to better prioritize and invest in evolving business and technology capabilities. Defining good metrics is not just about aligning to business goals and demonstrating value. It is also important to help prioritize strategic initiatives, guide effective resource allocation, and generate awareness across the organization to drive a shift in mindset with the new way of operating in the cloud.
The pandemic has accelerated the need for companies to modernize their digital capabilities. With technology-driven disruptions across all industries, it has never been more important for organizations to transform themselves, embrace an agile mindset, and make bold investments in cloud technology and capabilities to achieve sustainable business outcomes.
So, where are you now in your cloud FinOps journey, and how do you move beyond the challenges ahead? Google can help you start the conversation and accelerate your path to maximizing business value with the cloud.
No matter where you are on the cloud transformation journey, through an interactive session with Google, we can bring executives across the organization together to work toward a shared vision and a plan to accelerate and realize business value in the cloud. If you are interested in more information, please contact us.
Special thanks to Pathik Sharma, Bruce Warner, Jon Naseath, and Nihar Jhawar for their contributions and sharing their domain expertise to this important Cloud FinOps topic.
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Predict User Churn on Gaming Apps with Google Analytics Data using BigQuery ML
User retention can be a major challenge for mobile game developers. According to the Mobile Gaming Industry Analysis in 2019, most mobile games only see a 25% retention rate for users after the first day. To retain a larger percentage of users after their first use of an app, developers can take steps to motivate and incentivize certain users to return. But to do so, developers need to identify the propensity of any specific user returning after the first 24 hours.
In this blog post, we will discuss how you can use BigQuery ML to run propensity models on Google Analytics 4 data from your gaming app to determine the likelihood of specific users returning to your app.
You can also use the same end-to-end solution approach in other types of apps using Google Analytics for Firebase as well as apps and websites using Google Analytics 4. To try out the steps in this blogpost or to implement the solution for your own data, you can use this Jupyter Notebook.
Using this blog post and the accompanying Jupyter Notebook, you’ll learn how to:
- Explore the BigQuery export dataset for Google Analytics 4
- Prepare the training data using demographic and behavioural attributes
- Train propensity models using BigQuery ML
- Evaluate BigQuery ML models
- Make predictions using the BigQuery ML models
- Implement model insights in practical implementations
Google Analytics 4 (GA4) properties unify app and website measurement on a single platform and are now default in Google Analytics. Any business that wants to measure their website, app, or both, can use GA4 for a more complete view of how customers engage with their business. With the launch of Google Analytics 4, BigQuery export of Google Analytics data is now available to all users. If you are already using a Google Analytics 4 property, you can follow this guide to set up exporting your GA data to BigQuery.
Once you have set up the BigQuery export, you can explore the data in BigQuery. Google Analytics 4 uses an event-based measurement model. Each row in the data is an event with additional parameters and properties. The Schema for BigQuery Export can help you to understand the structure of the data.
In this blogpost, we use the public sample export data from an actual mobile game app called “Flood It!” (Android, iOS) to build a churn prediction model. But you can use data from your own app or website.
Here’s what the data looks like. Each row in the dataset is a unique event, which can contain nested fields for event parameters.
SELECT *FROM `firebase-public-project.analytics_153293282.events_*`TABLESAMPLE SYSTEM (1 PERCENT)
This dataset contains 5.7M events from over 15k users.
SELECTCOUNT(DISTINCT user_pseudo_id) as count_distinct_users,COUNT(event_timestamp) as count_eventsFROM`firebase-public-project.analytics_153293282.events_*
Our goal is to use BigQuery ML on the sample app dataset to predict propensity to user churn or not churn based on users’ demographics and activities within the first 24 hours of app installation.
In the following sections, we’ll cover how to:
- Pre-process the raw event data from GA4
- Identify users & the label feature
- Process demographic features
- Process behavioral features
- Train classification model using BigQuery ML
- Evaluate the model using BigQueryML
- Make predictions using BigQuery ML
- Utilize predictions for activation
Pre-process the raw event data
You cannot simply use raw event data to train a machine learning model as it would not be in the right shape and format to use as training data. So in this section, we’ll go through how to pre-process the raw data into an appropriate format to use as training data for classification models.
This is what the training data should look like for our use case at the end of this section:
Notice that in this training data, each row represents a unique user with a distinct user ID (user_pseudo_id).
Identify users & the label feature
We first filtered the dataset to remove users who were unlikely to return the app anyway. We defined these ‘bounced’ users as ones who spent less than 10 mins with the app. Then we labeled all remaining users:
- churned: No event data for the user after 24 hours of first engaging with the app.
- returned: The user has at least one event record after 24 hours of first engaging with the app.
For your use case, you can have a different definition of bounce and churning. Also you can even try to predict something else other than churning, e.g.:
- whether a user is likely to spend money on in-game currency
- likelihood of completing n-number of game levels
- likelihood of spending n amount of time in-game etc.
In such cases, label each record accordingly so that whatever you are trying to predict can be identified from the label column.
From our dataset, we found that ~41% users (5,557) bounced. However, from the remaining users (8,031), ~23% (1,883) churned after 24 hours:
SELECTbounced,churned,COUNT(churned) as count_usersFROMbqmlga4.returningusersGROUP BY 1,2ORDER BY bounced
To create these bounced and churned columns, we used the following snippet of SQL code.
...#churned = 1 if last_touch within 24 hr of app installation, else 0IF (user_last_engagement < TIMESTAMP_ADD(user_first_engagement,INTERVAL 24 HOUR),1,0 ) AS churned,#bounced = 1 if last_touch within 10 min, else 0IF (user_last_engagement <= TIMESTAMP_ADD(user_first_engagement,INTERVAL 10 MINUTE),1,0 ) AS bounced,...
You can view the Jupyter Notebook for the full query used for materializing the bounced and churned labels.
Process demographic features
Next, we added features both for demographic data and for behavioral data spanning across multiple columns. Having a combination of both demographic data and behavioral data helps to create a more predictive model.
We used the following fields for each user as demographic features:
geo.countrydevice.operating_systemdevice.language
A user might have multiple unique values in these fields — for example if a user uses the app from two different devices. To simplify, we used the values from the very first user engagement event.
CREATE OR REPLACE VIEW bqmlga4.user_demographics AS (WITH first_values AS (SELECTuser_pseudo_id,geo.country as country,device.operating_system as operating_system,device.language as language,ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC) AS row_numFROM `firebase-public-project.analytics_153293282.events_*`WHERE event_name="user_engagement")SELECT * EXCEPT (row_num)FROM first_valuesWHERE row_num = 1 #first engagement);
Process behavioral features
There is additional demographic information present in the GA4 export dataset, e.g. app_info, device, event_params, geo etc. You may also send demographic information to Google Analytics through each hit via user_properties. Furthermore, if you have first-party data on your own system, you can join that with the GA4 export data based on user_ids.
To extract user behavior from the data, we looked into the user’s activities within the first 24 hours of first user engagement. In addition to the events automatically collected by Google Analytics, there are also the recommended events for games that can be explored to analyze user behavior. For our use case, to predict user churn, we counted the number of times the follow events were collected for a user within 24 hours of first user engagement:
user_engagementlevel_start_quickplaylevel_end_quickplaylevel_complete_quickplaylevel_reset_quickplaypost_scorespend_virtual_currencyad_rewardchallenge_a_friendcompleted_5_levelsuse_extra_steps
The following query shows how these features were calculated:
WITHevents_first24hr AS (SELECTe.*FROM`firebase-public-project.analytics_153293282.events_*` eJOINbqmlga4.returningusers rONe.user_pseudo_id = r.user_pseudo_idWHERETIMESTAMP_MICROS(e.event_timestamp) <= r.ts_24hr_after_first_engagement)SELECTuser_pseudo_id,SUM(IF(event_name = 'user_engagement', 1, 0)) AS cnt_user_engagement,# ... repeated for all behavior data ...SUM(IF(event_name = 'use_extra_steps', 1, 0)) AS cnt_use_extra_steps,FROMevents_first24hrGROUP BY1
View the notebook for the query used to aggregate and extract the behavioral data. You can use different sets of events for your use case. To view the complete list of events, use the following query:
SELECTevent_name,COUNT(event_name) as event_countFROM`firebase-public-project.analytics_153293282.events_*`GROUP BY 1ORDER BYevent_count DESC
After this we combined the features to ensure our training dataset reflects the intended structure. We had the following columns in our table:
- User ID:
user_pseudo_id
- Label:
churned
- Demographic features
countrydevice_osdevice_language
- Behavioral features
cnt_user_engagementcnt_level_start_quickplaycnt_level_end_quickplaycnt_level_complete_quickplaycnt_level_reset_quickplaycnt_post_scorecnt_spend_virtual_currencycnt_ad_rewardcnt_challenge_a_friendcnt_completed_5_levelscnt_use_extra_stepsuser_first_engagement
At this point, the dataset was ready to train the classification machine learning model in BigQuery ML. Once trained, the model will output a propensity score between churn (churned=1) or return (churned=0) indicating the probability of a user churning based on the training data.
Train classification model
When using the CREATE MODEL statement, BigQuery ML automatically splits the data between training and test. Thus the model can be evaluated immediately after training (see the documentation for more information).
For the ML model, we can choose among the following classification algorithms where each type has its own pros and cons:
Often logistic regression is used as a starting point because it is the fastest to train. The query below shows how we trained the logistic regression classification models in BigQuery ML.
CREATE OR REPLACE MODEL bqmlga4.churn_logregTRANSFORM(EXTRACT(MONTH from user_first_engagement) as month,EXTRACT(DAYOFYEAR from user_first_engagement) as julianday,EXTRACT(DAYOFWEEK from user_first_engagement) as dayofweek,EXTRACT(HOUR from user_first_engagement) as hour,* EXCEPT(user_first_engagement, user_pseudo_id))OPTIONS(MODEL_TYPE="LOGISTIC_REG",INPUT_LABEL_COLS=["churned"]) ASSELECT*FROMbqmlga4.train
We extracted month, julianday, and dayofweek from datetimes/timestamps as one simple example of additional feature preprocessing before training. Using TRANSFORM() in your CREATE MODEL query allows the model to remember the extracted values. Thus, when making predictions using the model later on, these values won’t have to be extracted again. View the notebook for the example queries to train other types of models (XGBoost, deep neural network, AutoML Tables).
Evaluate model
Once the model finished training, we ran ML.EVALUATE to generate precision, recall, accuracy and f1_score for the model:
SELECT*FROMML.EVALUATE(MODEL bqmlga4.churn_logreg)
The optional THRESHOLD parameter can be used to modify the default classification threshold of 0.5. For more information on these metrics, you can read through the definitions on precision and recall, accuracy, f1-score, log_loss and roc_auc. Comparing the resulting evaluation metrics can help to decide among multiple models.Furthermore, we used a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels. The confusion matrix is created using the default threshold of 0.5, which you may want to adjust to optimize for recall, precision, or a balance (more information here).
SELECTexpected_label,_0 AS predicted_0,_1 AS predicted_1FROMML.CONFUSION_MATRIX(MODEL bqmlga4.churn_logreg)
This table can be interpreted in the following way:
Make predictions using BigQuery ML
Once the ideal model was available, we ran ML.PREDICT to make predictions. For propensity modeling, the most important output is the probability of a behavior occurring. The following query returns the probability that the user will return after 24 hrs. The higher the probability and closer it is to 1, the more likely the user is predicted to return, and the closer it is to 0, the more likely the user is predicted to churn.
SELECTuser_pseudo_id,returned,predicted_returned,predicted_returned_probs[OFFSET(0)].prob as probability_returnedFROMML.PREDICT(MODEL bqmlga4.churn_logreg,(SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset
Utilize predictions for activation
Once the model predictions are available for your users, you can activate this insight in different ways. In our analysis, we used user_pseudo_id as the user identifier. However, ideally, your app should send back the user_id from your app to Google Analytics. In addition to using first-party data for model predictions, this will also let you join back the predictions from the model into your own data.
- You can import the model predictions back into Google Analytics as a user attribute. This can be done using the Data Import feature for Google Analytics 4. Based on the prediction values you can Create and edit audiences and also do Audience targeting. For example, an audience can be users with prediction probability between 0.4 and 0.7, to represent users who are predicted to be “on the fence” between churning and returning.
- For Firebase Apps, you can use the Import segments feature. You can tailor user experience by targeting your identified users through Firebase services such as Remote Config, Cloud Messaging, and In-App Messaging. This will involve importing the segment data from BigQuery into Firebase. After that you can send notifications to the users, configure the app for them, or follow the user journeys across devices.
- Run targeted marketing campaigns via CRMs like Salesforce, e.g. send out reminder emails.
You can find all of the code used in this blogpost in the Github repository:
What’s next?
Continuous model evaluation and re-training
As you collect more data from your users, you may want to regularly evaluate your model on fresh data and re-train the model if you notice that the model quality is decaying.
Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.
To learn more about how to do continuous model evaluation and re-train models, you can read the blogpost: Continuous model evaluation with BigQuery ML, Stored Procedures, and Cloud Scheduler
More resources
If you’d like to learn more about any of the topics covered in this post, check out these resources:
- BigQuery export of Google Analytics data
- BigQuery ML quickstart
- Events automatically collected by Google Analytics 4
- Qwiklabs: Create ML models with BigQuery ML
Or learn more about how you can use BigQuery ML to easily build other machine learning solutions:
- How to build demand forecasting models with BigQuery ML
- How to build a recommendation system on e-commerce data using BigQuery ML
Let us know what you thought of this post, and if you have topics you’d like to see covered in the future! You can find us on Twitter at @polonglin and @_mkazi_.Thanks to reviewers: Abhishek Kashyap, Breen Baker, David Sabater Dinter.
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When moving to the cloud, many organizations concentrate their focus on the change in technology and overlook an area just as complex and impactful: cultural change. Having your people ready to embrace the change — supporting them with the right processes, equipping them with the right skills — is as important as getting the technology right.
To realize the full value of cloud technologies, many organizations are rethinking their IT organizational structure. There are a variety of potential talent implications too — from adopting agile ways of working to hiring for more cloud-centric skills to looking at redeploying current IT skills and reskilling and upskilling current teams.
As one of the organizations that pioneered hyperscale infrastructure, which led to the creation of the cloud, Google has spent years nurturing its culture and workforce to best operate in the cloud. We leverage this experience every day to help organizations ready their workforce for the change, and in this whitepaper, we aim to pass that experience along to you.
Answering the 4 Common FAQs on Compute Engine
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Compute Engine lets you create and run virtual machines (VMs) on Google’s infrastructure, allowing you to launch large compute clusters with ease. When it comes to getting started with Compute Engine, our customers have lots of questions—but some questions come up more often than others.
We looked at an internal list of the most popular Compute Engine documentation pages over a 30-day period to find out what topics were explored by users again and again. Here are the top four questions users have about Compute Engine, in order.
1. What are the different machine families
Compute Engine lets you select the right machine for your needs. You can choose from a curated set of predefined virtual machine (VM) configurations optimized for specific workloads, ranging from small-level purpose to large-scale use cases or create a machine type customized to your needs with our custom machine type feature.
Compute Engine machines are categorized by machine family, including:
- General-purpose: Best price-performance ratio for a variety of standard and cloud-native workloads
- Compute-optimized: Highest performance per core for compute-intensive workloads, such as ad serving or media transcoding
- Memory-optimized: More compute and memory per core than any other family for memory-intensive workloads, such as SAP HANA or in-memory data analytics
- Accelerator-optimized: Designed for your most demanding workloads, such as machine learning (ML) or high performance computing (HPC)
Read the documentation to learn more about each machine family category.
2. How to connect to VMs using advanced methods
In general, we recommend using the Google Cloud Console and the gcloud command-line tool to connect to Linux VM instances. However, some of our customers want to use third-party tools, or require alternative connection configurations.
In these cases, there are several methods that might fit your needs better than the standard connection options:
- Connecting to instances using third-party tools (e.g. Windows PuTTY, Chrome OS Secure Shell app), or MacOS or Linux local terminal)
- Connecting to instances without external IP addresses
- Connecting to instances as the root user Manually connecting between instances and running commands as a service account
Read the documentation to learn about advanced methods for connecting Linux VMs.
3. How to set up OS Login
OS Login lets you use IAM roles and permissions to manage access and permissions to VMs.
OS Login is the recommended way to manage users across multiple instances or projects. OS Login provides:
- Automatic Linux account lifecycle management
- Fine-grained authorization using Google IAM without having to grant broader privileges
- Automatic permissions updates to prevent unwanted access
- Ability to import existing Linux accounts from Active Directory (AD) and Lightweight Directory Access Protocol (LDAP)
You can also add an extra layer of security by setting up OS Login with two-factor authentication or manage organization access by setting up organization policies.
Read the documentation to learn how to configure OS login and connect to your instances.
4. How to manage SSH keys in metadata
Compute Engine allows you to manually manage SSH keys and local user accounts by editing public SSH key metadata.
You can add public SSH keys to instance and project metadata using:
- The Google Cloud Console The gcloud command-line tool
- API methods from the Google Cloud Client Libraries
Read the documentation to learn how to manually manage SSH keys and local user accounts in metadata.
Don’t see your question here? Check out the Compute Engine documentation for all of our recommended guides, tutorials, and resources.
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