Mindful Containers is a fictitious company that is creating containerized microservice applications. They need a fully managed compute environment for deploying and scaling serverless containerized microservices. So, they are considering Cloud Run. 

They are excited about Cloud Run because it abstracts away the cluster configuration, monitoring, and management so they can focus on building the features for their apps. Cloud Run is a fully-managed compute environment for deploying and scaling serverless containerized microservices.

What is Cloud Run?

Cloud Run is a fully-managed compute environment for deploying and scaling serverless HTTP containers without worrying about provisioning machines, configuring clusters, or autoscaling.

• No vendor lock-in – Because Cloud Run takes standard OCI containers and implements the standard Knative Serving API, you can easily port over your applications to on-premises or any other cloud environment. 
• Fast autoscaling – Microservices deployed in Cloud Run scale automatically based on the number of incoming requests, without you having to configure or manage a full-fledged Kubernetes cluster. Cloud Run scales to zero— that is, uses no resources—if there are no requests.
• Split traffic – Cloud Run enables you to split traffic between multiple revisions, so you can perform gradual rollouts such as canary deployments or blue/green deployments.
• Custom domains – You can set up custom domain mapping in Cloud Run and it will provision a TLS certificate for your domain. 
• Automatic redundancy – Cloud Run offers automatic redundancy so you don’t have to worry about creating multiple instances for high availability

How to use Cloud Run

With Cloud Run, you write your code in your favorite language and/or use a binary library of your choice. Then push it to Cloud Build to create a container build. With a single command—“gcloud run deploy”—you go from a container image to a fully managed web application that runs on a domain with a TLS certificate and auto-scales with requests.

How does Cloud Run work?

Cloud Run service can be invoked in the following ways:

HTTPS: You can send HTTPS requests to trigger a Cloud Run-hosted service. Note that all Cloud Run services have a stable HTTPS URL. Some use cases include: 

• Custom RESTful web API
• Private microservice
• HTTP middleware or reverse proxy for your web applications
• Prepackaged web application

gRPC: You can use gRPC to connect Cloud Run services with other services—for example, to provide simple, high-performance communication between internal microservices. gRPC is a good option when you: 

• Want to communicate between internal microservices
• Support high data loads (gRPC uses protocol buffers, which are up to seven times faster than REST calls)
• Need only a simple service definition you don’t want to write a full client library
• Use streaming gRPCs in your gRPC server to build more responsive applications and APIs

WebSockets: WebSockets applications are supported on Cloud Run with no additional configuration required. Potential use cases include any application that requires a streaming service, such as a chat application.

Trigger from Pub/Sub: You can use Pub/Sub to push messages to the endpoint of your Cloud Run service, where the messages are subsequently delivered to containers as HTTP requests. Possible use cases include:

• Transforming data after receiving an event upon a file upload to a Cloud Storage bucket
• Processing your Google Cloud operations suite logs with Cloud Run by exporting them to Pub/Sub
• Publishing and processing your own custom events from your Cloud Run services

Running services on a schedule: You can use Cloud Scheduler to securely trigger a Cloud Run service on a schedule. This is similar to using cron jobs. Possible use cases include:

• Performing backups on a regular basis
• Performing recurrent administration tasks, such as regenerating a sitemap or deleting old data, content, configurations, synchronizations, or revisions
• Generating bills or other documents

Executing asynchronous tasks: You can use Cloud Tasks to securely enqueue a task to be asynchronously processed by a Cloud Run service. Typical use cases include:

• Handling requests through unexpected production incidents
• Smoothing traffic spikes by delaying work that is not user-facing
• Reducing user response time by delegating slow background operations, such as database updates or batch processing, to be handled by another service, 
• Limiting the call rate to backend services like databases and third-party APIs

Events from Eventrac: You can trigger Cloud Run with events from more than 60 Google Cloud sources. For example:

• Use a Cloud Storage event (via Cloud Audit Logs) to trigger a data processing pipeline 
• Use a BigQuery event (via Cloud Audit Logs) to initiate downstream processing in Cloud Run each time a job is completed

How is Cloud Run different from Cloud Functions?

Cloud Run and Cloud Functions are both fully managed services that run on Google Cloud’s serverless infrastructure, auto-scale, and handle HTTP requests or events. They do, however, have some important differences:

• Cloud Functions lets you deploy snippets of code (functions) written in a limited set of programming languages, while Cloud Run lets you deploy container images using the programming language of your choice. 
• Cloud Run also supports the use of any tool or system library from your application; Cloud Functions does not let you use custom executables. 
• Cloud Run offers a longer request timeout duration of up to 60 minutes, while with Cloud Functions the requests timeout can be set as high as 9 mins. 
• Cloud Functions only sends one request at a time to each function instance, while by default Cloud Run is configured to send multiple concurrent requests on each container instance. This is helpful to improve latency and reduce costs if you’re expecting large volumes. 

Pricing

Cloud Run comes with a generous free tier and is pay per use, which means you only pay while a request is being handled on your container instance. If it is idle with no traffic, then you don’t pay anything.

Conclusion

After learning about the ease of set up, scalability, and management capabilities of Cloud Run the Mindful Containers team is using it to deploy stateless microservices. If you are interested in learning more, check out the documentation.https://www.youtube.com/embed/oR4btKLRdn4?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev

Scaling Virtual Support in the Pandemic Era: The AI Connection

Since the early days of the pandemic, we’ve partnered with government organizations and academic institutions to serve communities at scale with Contact Center Artificial Intelligence (CCAI). I sat down with Bill MacKenzie, IT liaison for the Upper Grand School District in Ontario, and Marco Palermo, director of digital government and modernization, to discuss how they embraced CCAI to introduce scalable service delivery to residents and students alike. I’m sharing more on their stories below, and for the full overview, check out our Google Cloud Public Sector Summit session, Scaling Virtual Support in the Pandemic Era: The AI Connection.

Upper Grand School District: Answering questions with speed and accuracy

Bill MacKenzie described the Upper Grand School District’s struggles at the beginning of the pandemic, particularly helping parents with IT issues. The staff-oriented help desk was not equipped to assist parents trying to securely login for students as young as kindergarten. Without sufficient support for parents, the District was struggling to handle thousands of phone calls a day.

To alleviate the manual strain, they turned to Quantiphi, a Google Cloud partner, to implement Google Cloud Dialogflow. They were fully functional within a few weeks. The new website provided real-time responses as well as clear documentation to help parents get up and running quickly.

“In the first 10 days, we had over 5,000 hits, and the accuracy rate was 92%,” MacKenzie said. 

The district doesn’t know what the future holds, but now that they have been through the process, they are confident they now understand how to create their own bots to meet critical needs.

City of Toronto: Getting critical information to the community

Marco Palermo explained how the city of Toronto was facing a very rapid and fluid situation at the beginning of the pandemic. Getting information to constituents was extremely important, and they needed alternative channels to deliver that information.

Toronto has been committed to workforce equity and inclusivity in order to best represent the diversity of its residents. As a result, solutions had to be accessible to all. The bot handled 25,330 unique users and addressed 20,174 total questions with an 80% accurate response rate in the first four months. It’s been a huge success for the city, with plans to expand its capabilities.

Personalized response to the pandemic challenge

I noted that even before the pandemic, government leaders were asking for a way to provide more flexible personal experiences and better support outside of normal business hours. Around 60% of constituents want more self-service options and 75% would happily interact with digital resources if they could get the answers they needed.

Google Cloud CCAI  addresses these needs with a single core of intelligence that provides a consistently high-quality conversational experience—human or virtual—across all channels and platforms. It can be deployed on legacy infrastructure without upgrades in an average of two weeks.

CCAI operates with a conversational core that centralizes the ability to talk, understand, and interact, orchestrating high-quality conversations at scale. It provides services in three different ways:

• Virtual Agent AI allows natural conversation with customers to identify and address their issues effectively
• Agent Assist AI helps human agents by providing real-time turn-by-turn guidance so they can better serve constituents
• Insight AI determines metrics and trends in real-time to enable faster and more accurate insights

CCAI provides consistency across every application. It can go off-script when answering complex questions, adjusting human conversation with the capacity to handle unexpected stops and starts, odd word choices, or implied meanings. It can also handle multiple use cases for the customer, such as taking payments, updating information, providing information, and more. By allowing agencies to automate routine tasks and reduce the amount of time employees need to dedicate to answering calls, the solution created cost savings for the agency.

Explore more on this session by visiting the Public Sector Summit on-demand video.

Time to go outside again, I guess. I’ll need a sun hat. Sunscreen. Maybe some new sandals? What else?

With the Recommendations AI service, I might be reminded to grab a reusable water bottle and a swimsuit. Or some after-sun aloe lotion. Good thing, cause I’ll need it.

Recommendations AI is a solution that uses machine learning to bring product recommendations to their shoppers across any catalog or client list. This service is part of our full suite of Retail solutions. When you integrate with the Retail API, you get the benefit of Google’s Product Discovery. Integrating once to reap the benefits over and over. Recommendations is the starting point, and you can easily extend into Retail Search and Vision Product Search in the upcoming future. 

The Recommendations solution is fully managed, global-scale and powered by deep learning, so you can focus on a great shopping experience and let someone else worry about the infrastructure.

Compared to baseline recommendation systems used by customers, Recommendations AI showed double digit uplift in conversion and clickthrough rates in A/B experiments controlled by the customers. You can optimize for click-through, conversion or session revenue, and fine tune the models to make sure you omit out-of-stock items or duplicates, for example.

So how does it work, and how do you get started? Read on, and we’ll walk you through the pipeline, starting with the data you already have to placement in your online store. 

Formula: Data -> Model -> Placement

You start with your catalog, the list of all the things (postcards, movies, pie recipes) that you want to show your customers. Then you ingest your PII-redacted user events -this is the historic event data like home page views, add to cart events and more along with real time user events. This user event is joined with the product catalog and items that allows us to construct the sequence of shoppers’ activity, thus being able to predict what the shopper has a high propensity to purchase next. The user events can come from both online activity across devices or offline store purchases

The recommendation model will return a list of products, which are the recommendations. The brains of the operation, if you will. This model is trained using all the data that you ingest, using the latest neural network models and techniques that Google has built expertise over the years in flagship products like Youtube and News, that allows us to uncover shopper intent,  so it can best predict the right recommendations to show to the right people.

Every model outputs a list of product identifiers, but where do they go? They go into placements, the spots, panels, carousels on your customer’s journey interacting with your brand that you’ve set aside to highlight recommendations. A model can send recommendations to one or more placements, but each placement only receives information from one recommendation model. Your pages will then need to render the products with the right images, text or other metadata, using the product ID that is returned by the model.

What do recommendations look like?

Let’s start by browsing our postcard-selling website, where I’ve been buying some vintage California postcards already. The recommendations algorithm has caught on to my interest, showing me other potential cards to purchase based on my history:

Put your data to work

To get started we need to bring your data into the recommendation model, so it can understand your customers, your inventory, and your sales patterns. 

The model takes in the product catalog you use, and metadata about those products to better understand nuances in assortment, pricing and variables like size and style. You might already have this data stored in BigQuery or Merchant Center, and hence we provide easy integrations that you can leverage to get started even faster.

As for the user events, don’t worry if you already have systems in place to capture web and mobile activity. We make it easy to bring in your real time event logs by providing seamless integrations with Google Tag Manager, Javascript pixel, or even historic events from Cloud Storage, BigQuery or using inline API or JSON, so you can immediately train the models on this imported  data. All this allows you to kickstart integrating with Recommendations AI in a matter of days.

The models then construct a sequence of activities that the user went through and joins with the products that the user engaged with. Once your data is ready to go, it takes a few days to train the model. Next onto making the data work for you.

Quickly customize your model

Setting up your own recommendations project in the console gives you the ability to choose what sort of model to train (based on what recommendations you want to generate) and your objective. Are you optimizing for click-through rate–more people click on the recommendation links or products–or for conversion rate–more people choose or buy what was suggested or revenue ?

Different models can be optimized for different optimization goals.; the GCP console explains what each one can do and how you can choose to optimize it.

Let’s unpack some of this terminology real quick.

We’ve got three model types:

• Recommended for you – Means we think these are items you’ll want to buy, based on your history; this is usually used on a home page to showcase items.
• Others you may like – Means if you’re browsing the page of a water bottle, we will recommend  alternative brands of water bottles that you may like as well as a backpack, based on your engagement  history.
• Frequently bought together – Means that when anyone buys sunscreen, we notice that they often also buy aloe lotion, so we will surface those items when someone adds any one of them to their cart.

And then we have three business objectives that the models optimize for:

• Click-through rate – How frequently did somebody click on a recommended item?
• Conversion rate– How frequently did somebody add a recommended item to their cart?
• Revenue per session – How much money did the recommendations generate for you?

Deliver anywhere along the journey

Now that you’re all set up in the Retail AI console, you can test out the recommendations in the console, even before you deploy to production.

You can integrate Recommendations into your frontend by calling the Predict APIt. The placements of recommendations will report data back into the dashboard and you can analyze and measure success for future iterations. 

On top of that you can use the recommendations for other parts of your customer’s journey. Email promotions, storefront kiosks, display ads or follow-up notifications can include recommendations based on past activity and cart contents. The model gives you useful product recommendations for a wide variety of touchpoints and steps in the purchasing process.

More best practices, and guides, are available inside our documentation.

How to get started

Training your own models can be tedious, time-consuming, and expensive. On top of that it requires deeper data science expertise to set up. Let us do it instead!

You can see how IKEA Retail uses Recommendations AI in this recent talk and blog from the Google Cloud Retail Summit..

To get started today you’ll need to make a Cloud project and enable the Retail API, which then allows you to access all the recommendation tools in one menu. Bring in your catalog and purchasing data, define a placement or two, and you can start putting recommendations on your site in a matter of days.