Reasons to Leverage Vertex AI Custom Training Service
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At one point or another, many of us have used a local computing environment for machine learning (ML). That may have been a notebook computer or a desktop with a GPU. For some problems, a local environment is more than enough. Plus, there’s a lot of flexibility. Install Python, install JupyterLab, and go!
What often happens next is that model training just takes too long. Add a new layer, change some parameters, and wait nine hours to see if the accuracy improved? No thanks. By moving to a Cloud computing environment, a wide variety of powerful machine types are available. That same code might run orders of magnitude faster in the Cloud.
Customers can use Deep Learning VM images (DLVMs) that ensure that ML frameworks, drivers, accelerators, and hardware are all working smoothly together with no extra configuration. Notebook instances are also available that are based on DLVMs, and enable easy access to JupyterLab.
Benefits of using the Vertex AI custom training service
Using VMs in the cloud can make a huge difference in productivity for ML teams. There are some great reasons to go one step further, and leverage our new Vertex AI custom training service. Instead of training your model directly within your notebook instance, you can submit a training job from your notebook.
The training job will automatically provision computing resources, and de-provision those resources when the job is complete. There is no worrying about leaving a high-performance virtual machine configuration running.
The training service can help to modularize your architecture. As we’ll discuss further in this post, you can put your training code into a container to operate as a portable unit. The training code can have parameters passed into it, such as input data location and hyperparameters, to adapt to different scenarios without redeployment. Also, the training code can export the trained model file, enabling working with other AI services in a decoupled manner.
The training service also supports reproducibility. Each training job is tracked with inputs, outputs, and the container image used. Log messages are available in Cloud Logging, and jobs can be monitored while running.
The training service also supports distributed training, which means that you can train models across multiple nodes in parallel. That translates into faster training times than would be possible within a single VM instance.
Example Notebook
In this blog post, we are going to explain how to use the custom training service, using code snippets from a Vertex AI example. The notebook we’re going to use covers the end-to-end process of custom training and online prediction. The notebook is part of the ai-platform-samples repo, which has many useful examples of how to use Vertex AI.
Custom model training concepts
The custom model training service provides pre-built container images supporting popular frameworks such as TensorFlow, PyTorch, scikit-learn, and XGBoost. Using these containers, you can simply provide your training code and the appropriate container image to a training job.
You are also able to provide a custom container image. A custom container image can be a good choice if you’re using a language other than Python, or are using an ML framework that is not supported by a pre-built container image. In this blog post, we’ll use a pre-built TensorFlow 2 image with GPU support.
There are multiple ways to manage custom training jobs: via the Console, gcloud CLI, REST API, and Node.js / Python SDKs. After jobs are created, their current status can be queried, and the logs can be streamed.
The training service also supports hyperparameter tuning to find optimal parameters for training your model. A hyperparameter tuning job is similar to a custom training job, in that a training image is provided to the job interface. The training service will run multiple trials, or training jobs with different sets of hyperparameters, to find what results in the best model. You will need to specify the hyperparameters to test; the range of values to explore for those hyperparameters; and details about the number of trials.
Both custom training and hyperparameter tuning jobs can be wrapped into a training pipeline. A training pipeline will execute the job, and can also perform an optional step to upload the model to Vertex AI after training.
How to package your code for a training job
In general, it’s a good practice to develop your model training code that is self-contained when especially executing them inside containers. This means the training codebase would operate in a standalone manner when executed.
Below is a template of such a self-contained, heavily-commented Python script that you can follow for your own projects too.
# Imports go hereimport tensorflow_datasets as tfdsimport tensorflow as tf…# Define the hyperparameters and constants like epochs, batch size, number of GPUs, etcparser = argparse.ArgumentParser()parser.add_argument('--lr', dest='lr',default=0.01, type=float,help='Learning rate.')parser.add_argument('--epochs', dest='epochs',default=10, type=int,help='Number of epochs.')...args = parser.parse_args()...# Prepare data loadersdef make_datasets_unbatched():# Scaling CIFAR10 data from (0, 255] to (0., 1.]def scale(image, label):image = tf.cast(image, tf.float32)image /= 255.0return image, labeldatasets, info = tfds.load(name='cifar10',with_info=True,as_supervised=True)return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()# Build our model, compile, and train itmodel = [define your model]model.compile(loss=..., optimizer=..., metrics=...)model.fit(...)# Serialize our modelmodel.save(MODEL_DIR)
Note that the MODEL_DIR needs to be a location inside a Google Cloud Storage (GCS) bucket. This is because the training service can only communicate with that and not with our local system. Here is a sample location inside a GCS Bucket to save a model: gs://caip-training/cifar10-model where caip-training is the name of the GCS bucket.
Although we are not using any custom modules in the above code listing, one can easily incorporate them as we would normally inside a Python script. Refer to this document if you want to know more. Next up, we will review how to configure the training infrastructure, including the type and number of GPUs to use, and submit a training script to run inside the infrastructure.
How to submit a training job, including configuring which machines to use
To train a deep learning model efficiently on large datasets, we need hardware accelerators that are suited to run matrix multiplication in a highly parallelized manner. Distributed training is also common when it comes to training a large model on a large dataset. For this example, we will be using a single Tesla K80 GPU. Vertex AI supports a range of different GPUs (find out more here).
Here is how we initialize our training job with the Vertex AI SDK:
job = aiplatform.CustomTrainingJob(display_name=JOB_NAME,script_path="task.py",container_uri=TRAIN_IMAGE,requirements=["tensorflow_datasets==1.3.0"],model_serving_container_image_uri=DEPLOY_IMAGE,)
(aiplatform is aliased as from google.cloud import aiplatform)
Let’s review the arguments:
display_namerefers to a unique identifier to the training job used for easily locating it.script_pathrefers to the path of the training script to run. This is the script we discussed in the section above.container_urirefers to the URI of the container that will be used to run our training script. For this, we have several options to choose from. For this example, we will usegcr.io/cloud-aiplatform/training/tf-gpu.2-1:latest. We will use this same container for deployment as well but with a slightly changed container URI. You can find the containers available for model training here and the containers available for deployment purposes can be found here.requirementslet us specify any external packages that might be required to run the training script.model_serving_container_image_urispecifies the container URI that would be used during deployment.
Note: Using separate containers for distinct purposes like training and deployment is often a good practice, since it isolates the relevant dependencies for each purpose.
We are now all set up to submit a custom training job:
model = job.run(model_display_name=MODEL_DISPLAY_NAME,args=CMDARGS,replica_count=1,machine_type=TRAIN_COMPUTE,accelerator_type=TRAIN_GPU.name,accelerator_count=TRAIN_NGPU)
Here, we have:model_display_name that provides a unique name to identify our trained model. This comes in handy later down the pipeline when we would deploy it using the prediction service. args are our command-line arguments typically used to specify things like hyperparameter values.replica_count denotes the number of worker replicas to be used during training. machine_type specifies the type of base machine to be used during training. accelerator_type denotes the type of accelerator to be used during training. If we are interested in using a Tesla K80, then TRAIN_GPU should be specified as aip.AcceleratorType.NVIDIA_TESLA_K80. (aip is aliased as from google.cloud.aiplatform import gapic as aip.)accelerator_count specifies the number of accelerators to use. For a single host multi-GPU configuration, we would set the replica_count to 1 and then specify the accelerator_count as per our choice depending on the resource available under the corresponding compute zone.
Note that model here is a google.cloud.aiplatform.models.Model object. It is returned by the training service after the job is completed.
With this setup, we can actually start a custom training job that we can monitor. After we submit the above training pipeline, we should see some initial logs resembling this:
The link highlighted in Figure 2 will redirect to the dashboard of the training pipeline which looks like so:
As seen in Figure 3, the dashboard provides a comprehensive summary of all the necessary artifacts related to our training pipeline. Monitoring your model training is also very important especially to catch any early training bugs. To view the training logs, we need to click the link beside the “Custom job” tab (refer to Figure 3). There also we are presented with roughly similar information as shown in Figure 3 but this time it includes the logs as well:
Note: Once we submit the custom training job, a training pipeline is first created to provision the training. Then inside the pipeline, the actual training job is started. This is why we see two very similar dashboards above but they have different purposes. Let’s check out the logs (which is maintained using Cloud Logging automatically):
With Cloud Logging, it is also possible to set alerts on the basis of different criteria. For example, alerting the users when the training job fails or completes so that some immediate action could be taken. You can refer to this post for more details.
After the training pipeline is completed, on your end, you will notice the success status:
Accessing the trained model
Recall that we had to serialize our model inside a GCS Bucket in order to make it compatible with the training service. So, after the model is trained, we can access it from that location. We can even directly load it using the following line of code:
model = tf.keras.models.load_model('gs://[your-bucket-name]/[model-name]')
Note that we are referring to the TensorFlow model that resulted from training. The training service also maintains a similar “model” namespace to help us manage these models. Recall that the training service returns a google.cloud.aiplatform.models.Model object as mentioned earlier. It comes with a deploy() method that allows us to deploy our model programmatically within minutes with several different options. Check out this link if you are interested in deploying your models using this option.
Vertex AI also provides a dashboard for all the models that have been trained successfully and it can be accessed with this link. It resembles this:
If we click the model as listed in Figure 7, we should be able to directly deploy from the interface:
In this post, we will not be covering deployment, but you are encouraged to try it out yourself. After the model is deployed to an endpoint, you will be able to use it to make online predictions.
Wrapping Up
In this blog post, we discussed the benefits of using the Vertex AI custom training service, including better reproducibility and management of experiments. We also walked through the steps to convert your Jupyter Notebook codebase to a standard containerized codebase, which will be useful not only for the training service, but for other container-based environments. The example notebook provides a great starting point to understand each step, and to use as a template for your own projects.
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How Marketers Can Turn Information into Action with Machine Learning
The biggest challenge marketers face with machine learning is, “how to get starter”? Instead of getting overwhelmed, they should focus on the applied machine learning by using the algorithms that are already built.
Cassie Kozyrkov, the chief decision scientist with Google Cloud, says that marketers who are overwhelmed by everything they’re hearing about machine learning should focus on key ingredients, not building an entire kitchen.
Transform ‘Dark Data’ from Documents with Document AI, Cloud Functions and Workflows
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At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data that businesses collect, process and store but do not utilize for purposes such as analytics, monetization, etc. These documents in pdf or image formats, often trigger complex processes that have historically relied on fragmented technology and manual steps. With compute solutions on Google Cloud and Document AI, you can create seamless integrations and easy to use applications for your users. Document AI is a platform and a family of solutions that help businesses to transform documents into structured data backed by machine learning. In this blog post we’ll walk you through how to use Serverless technology to process documents with Cloud Functions, and with workflows of business processes orchestrating microservices, API calls, and functions, thanks to Workflows.
At Cloud Next 2021, we presented how to build easy AI-powered applications with Google Cloud. We introduced a sample application for handling incoming expense reports, analyzing expense receipts with Procurement Document AI, a DocAI solution for automating procurement data capture from forms including invoices, utility statements and more. Then organizing the logic of a report approval process with Workflows, and used Cloud Functions as glue to invoke the workflow, and do analysis of the parsed document.
We also open sourced the code on this Github repository, if you’re interested in learning more about this application.
In the above diagram, there are two user journeys: the employee submitting an expense report where multiple receipts are processed at once, and the manager validating or rejecting the expense report.
First, the employee goes to the website, powered by Vue.js for the frontend progressive JavaScript framework and Shoelace for the library of web components. The website is hosted via Firebase Hosting. The frontend invokes an HTTP function that triggers the execution of our business workflow, defined using the Workflows YAML syntax.
Workflows is able to handle long-running operations without any additional code required, in our case we are asynchronously processing a set receipt files. Here, the Document AI connector directly calls the batch processing endpoint for service. This API returns a long-running operation: if you poll the API, the operation state will be “RUNNING” until it has reached a “SUCCEEDED” or “FAILED” state. You would have to wait for its completion. However, Workflows’ connectors handle such long-running operations, without you having to poll the API multiple times till the state changes. Here’s how we call the batch processing operation of the Document AI connector:
- invoke_document_ai:call: googleapis.documentai.v1.projects.locations.processors.batchProcessargs:name: ${"projects/" + project + "/locations/eu/processors/" + processorId}location: "eu"body:inputDocuments:gcsPrefix:gcsUriPrefix: ${bucket_input + report_id}documentOutputConfig:gcsOutputConfig:gcsUri: ${bucket_output + report_id}skipHumanReview: trueresult: document_ai_response
Machine learning uses state of the art Vision and Natural Language Processing models to intelligently extract schematized data from documents with Document AI. As a developer, you don’t have to figure out how to fine tune or reframe the receipt pictures, or how to find the relevant field and information in the receipt. It’s Document AI’s job to help you here: it will return a JSON document whose fields are: line_item, currency, supplier_name, total_amount, etc. Document AI is capable of understanding standardized papers and forms, including invoices, lending documents, pay slips, driver licenses, and more.
A cloud function retrieves all the relevant fields of the receipts, and makes its own tallies, before submitting the expense report for approval to the manager. Another useful feature of Workflows is put to good use: Callbacks, that we introduced last year. In the workflow definition we create a callback endpoint, and the workflow execution will wait for the callback to be called to continue its flow, thanks to those two instructions:
- create_callback:call: events.create_callback_endpointargs:http_callback_method: "POST"result: callback_details...- await_callback:try:call: events.await_callbackargs:callback: ${callback_details}timeout: 3600result: callback_requestexcept:as: esteps:- update_status_to_error:...
In this example application, we combined the intelligent capabilities of Document AI to transform complex image documents into usable structured data, with Cloud Functions for data transformation, process triggering, and callback handling logic, and Workflows enabled us to orchestrate the underlying business process and its service call logic.
Going further
If you’re looking to make sense of your documents, turning dark data into structured information, be sure to check out what Document AI offers. You can also get your hands on a codelab to get started quickly, in which you’ll get a chance at processing handwritten forms. If you want to explore Workflows, quickstarts are available to guide you through your first steps, and likewise, another codelab explores the basics of Workflows. As mentioned earlier, for a concrete example, the source code of our smart expense application is available on Github. Don’t hesitate to reach out to us at @glaforge and @asrivas_dev to discuss smart scalable apps with us.
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An Overview of Google’s Data Cloud
Data access, management and privacy has been at the center of priorities for enterprises that are aiming to be more agile, reliable and data-driven. Google Cloud’s technology innovations spanning products like BigQuery, Spanner, Looker and VertexAI help organizations navigate the complexities related to siloed data in large volumes sprawled across databases, data lakes, data warehouses, and data marts in multiple clouds and on-premises. Watch the video to learn how companies are building data on Google Cloud for better analysis, security and management to achieve bottomline!
Transforming Media Industry: Three Strategies for Media Leaders to Leverage Generative AI
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The digital era turned the traditional formula for media and entertainment success on its head, ushering in new technologies that have changed how content is produced, distributed, experienced, and monetized. Audiences have more choice, flexibility, and power over what they consume, and today’s media companies have to embrace ongoing transformation or risk falling behind – or becoming irrelevant.
A new wave of transformation is arriving with generative AI, a type of artificial intelligence that can interact with users in natural language and create novel data, ranging from story outlines, reports, and other text outputs to multimodal content like images, videos, and audio. Media and entertainment are inherently about content creation and creativity—so what does this new technology mean for the industry?
At Google Cloud, we see tremendous opportunity for creative industries, from more efficient creation methods to improved user experiences. Let’s explore.
AI for media with Google Cloud
Google Cloud has a long history with large language models (LLMs) and other generative AI technologies—from their influence over the years on products like Document AI, to recent announcements like Generative AI support in Vertex AI, which lets businesses access and tune generative AI foundation models, and Generative AI App Builder, which lets developers build chatbots and other generative apps in minutes.
We’ve helped our global media and entertainment customers with AI for personalization, search and recommendations, predictive analytics, and much more — and with generative AI now on the rise, we have some ideas to help media leaders, technologists, and creators think about and prepare to utilize powerful AI in their work.
Three lenses on innovation in media
The media and entertainment industry is increasingly diverse and complex, with companies spanning over-the-top (OTT) subscription streaming services, 24-hour linear channels, live broadcasts of sporting events, digital journalism, traditional publishing, short-form user-generated social video, and more. More and more, the boundaries between these segments of the media industry are blurring — but common to them all is the focus on providing compelling content in an engaging audience experience that can be directly or indirectly monetized.
With this in mind, we suggest media and entertainment companies look at the application of innovative technologies like generative AI through the following three lenses:
- Improving content creation, production, and management
- Enhancing and personalizing audience experiences
- Improving monetization
Improving content creation, production, and management
Generative AI democratizes many aspects of content creation, opening new ways to create written material, illustrations, sound effects, special effects, and more. Its recent maturation has been so rapid, some in the media industry have expressed concern that generative AI implies the end of creative professions. We think the opposite is more likely: just as photography, audio recordings, and computer generated images have enabled new modes of creativity, rather than making old ones obsolete, generative AI has the potential to both enable new forms of expression and enhance familiar ones.
For example, journalists could use generative AI to speed up research by helping them synthesize and analyze large volumes of information, or to help them create initial drafts or summaries of editorial content. Film and television producers could leverage the technology to accelerate the post-production editing process, with new AI-enabled interfaces for rapidly adjusting or enhancing scene details such as lighting and color. Broadcasters could use generative AI to make vast libraries of video footage searchable and accessible for use in telling more compelling stories. The potential use cases go on and on.
Far from undermining incredible creative professions, generative AI is poised to free writers, artists, editors, and many others from the tedious and mundane aspects of their work, empowering them to focus more of their time on creativity.
Enhancing and personalizing audience experiences
Every media organization in the world today faces the reality that for most consumers, switching costs are extremely low. This puts incredible pressure on these companies to invest in delivering low-friction and compelling audience experiences that help mitigate subscribers from churning and viewers from abandoning content experiences for competitive platforms.
Generative AI can help media companies engage and retain viewers, such as by enabling more powerful search and recommendations on their digital content platforms. With its increasingly multimodal capabilities extending from natural language to both audio and video content, generative AI is well-positioned to power more personalized audience experiences.
Consumers often complain about “the paradox of choice” or their inability to find something interesting to watch on streaming platforms that have incredibly vast libraries of content available on demand. Imagine a not-too-distant future wherein a consumer can simply ask the content platform they’re using to help them find a specific show to watch based on mood, specific types of scenes, combinations of actors, award nominations, or practically anything they can think to ask. And that’s just the tip of the iceberg — imagine generative AI’s potential to curate, assemble, and even create personalized content for a viewer to consume!
Improving monetization
As consumers’ content consumption further expands from traditional theatrical and linear television programming to include digital offerings across an array of platforms, devices, and content types, media companies face the challenge of maintaining and improving monetization. The conventional economics and approaches to advertising and subscription models are proving, in many cases, not to deliver sufficient ROI.
Generative AI has the potential to help media companies improve their monetization of audience experiences. As mentioned previously, enhanced personalization can play a role in mitigating churn, which in turn can help sustain and grow subscription and advertising revenues. Going beyond this, generative AI can be leveraged to drive even greater advertising revenues via more targeted, contextual, and personalized advertisements. Imagine both display and video advertisements that are generated on the fly to personalize product specifics, messaging, style, colors, and innumerable other characteristics to drive greater engagement and higher click-through rates (CTR), and thus higher advertising CPMs (cost per thousand impressions).
Coming up next
Generative AI presents a significant opportunity for media companies to fundamentally transform content creation, engagement, and monetization. Compelling services are already on the market — but there is far more to come.
Google Cloud continues to build on its deep experience and expertise with AI, and we are committed to working with the industry to develop compelling, accessible, trusted, and responsible AI solutions that will drive meaningful business outcomes. We are excited to create the future together with our global media customers and partners across the ecosystem. To learn more about this disruptive topic, read “Debunking five generative AI misconceptions” from Google Cloud vice president of AI & Business Solutions Phil Moyer, or explore our Trusted Tester Program for generative AI.
Google Cloud Celebrates Journey of 3 Inspiring Founders for the Asian Pacific American Heritage Month
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May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.
Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.
CultivatePeople
Founder: Lola Han
Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees.
Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.”
Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission.
My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and:
1) Enjoy the journey by celebrating wins and giving yourself credit;
2) Follow your intuition—it’s (almost) always right;
3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);
4) Give regular words of affirmation to employees on even small achievements.”
Swit
Founder: Josh Lee and Max Lim
Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.
Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”
Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.”
Check out more from Swit in their founder story.
WISY
Founder: Min Chen
Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.
Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.
Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“
Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.
My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”
If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.
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