As an enterprise admin, when you choose to use Google Cloud Private Catalog to enable curated, self-serve Google Cloud infrastructure provisioning, you need the ability to manage your organization’s deployments. Today, we’re pleased to announce support for several improvements to Terraform driven deployments through Private Catalog. 

With this new release, you can update Terraform configurations and keep your end users informed about updates. At the same time, Private Catalog users have the ability to view new updates, note version highlights and then update the deployment. This gives you greater control over managing deployments for solutions provisioned through Private Catalog and ensuring compliance with organizational policies and standards.

Let’s take a closer look at the features you’ll find in this release. 

Deployment change management

Terraform solutions use Cloud Storage’s Object Versioning to manage updates to configuration files. With this release, you may update configuration files using multiple approaches.

• Update the solution’s Cloud Storage object with a new configuration version
• Use a different Cloud Storage object that contains a new configuration file

Once you view and apply the changes to the solution in a Private Catalog, end users are immediately able to consume the new version of the deployment configuration.

Additionally, prior to applying any changes, you can evaluate the contents of an update by comparing versions to download and compare the current and latest versions of the configuration and use new version highlights to add a description about the updates.

Ease of consumption

Once Private Catalog detects a change to the deployment configuration, it automatically informs catalog users about the change. On the Solutions page, end users have the ability to:

• Get informed about solutions that have updates
• View version highlights published by the admin
• Apply the new version 

Additionally, with this release, Catalog users can retry existing deployments by modifying deployment parameters.

Reporting improvements

The deployment reporting dashboards for Private Catalog-based deployments now show additional information about the version of a solution deployed. This enables deeper insights into the overall deployment status across all Private Catalog solution assets.

Get started today

These new features are available to all Private Catalog customers. To learn how to use these features, refer to our documentation:

• Create a Terraform configuration in Private Catalog
• Manage and update your Terraform configurations in Private Catalog

We recently launched Vertex AI to help you move machine learning (ML) from experimentation into production faster and manage your models with confidence—speeding up your ability to improve outcomes at your organization.

But we know many of you are just getting started with ML and there’s a lot to learn! In tandem with building the Vertex AI platform, our teams are dropping as much best practices content as we can to help you come up to speed. Plus, we have a dedicated event on June 10th, Applied ML Summit, with sessions on how to apply ML technology in your projects, as well as grow your skills in this field. 

In the meantime, we couldn’t resist a quick lesson on hyperparameter tuning, because (a) it’s incredibly cool (b) you will impress your coworkers (c) Google Cloud has some unique battle tested tech in this area and (d) you will save time by getting better ML models into production faster. Vertex Vizier, on average, finds optimal parameters for complex functions in over 80% fewer trials than traditional methods. 

So it’s incredibly cool, but what is it?

While machine learning models automatically learn from data, they still require user-defined knobs which guide the learning process. These knobs, commonly known as hyperparameters, control, for example, the tradeoff between training accuracy and generalizability.  Examples of hyperparameters are the optimizer being used, its learning rate, regularization parameters, the number of hidden layers in a DNN, and their sizes.

Setting hyperparameters to their optimal values for a given dataset can make a huge difference in model quality. Typically, optimal hyperparameter values are found via grid searching a small number of combinations, or tedious manual experimentation. Hyperparameter tuning automates this work for you by searching for the best configuration of hyperparameters for optimal model performance. 

Vertex Vizier enables automated hyperparameter tuning in several ways:

1. “Traditional” hyperparameter tuning: by this we mean finding the optimal value of hyperparameters by measuring a single objective metric which is the output of an ML model.  For example, Vizier selects the number of hidden layers and their sizes, an optimizer and its learning rate, with the goal of maximizing model accuracy.
2. When hyperparameters are evaluated, models are trained and evaluated on splits of the data set. If evaluation metrics are streamed to Vizier (e.g. as a function of epoch) as the model is trained, Vizier’s early stopping algorithms can predict the final objective value, and recommend which unpromising trials should be early stopped. This conserves compute resources and speeds up convergence.
3. Oftentimes, models are tuned sequentially on different data sets. Vizier’s built in transfer learning learns priors from previous hyperparameter tuning studies, and leverages them to converge faster on subsequent hyperparameter tuning studies.
4. AutoML is a variant of #1, where Vertex Vizier performs both model selection, and also tunes architectures/non-architecture modifying hyperparameters. AutoML usually requires more code on top of Vertex Vizier (to ingest data etc), but Vizier is in most cases the “engine” behind the process. AutoML is implemented by defining a tree like (DAG) search space, rather than a “flat” search space (like in #1). Note that you can use DAG search spaces for any other purpose where searching over a hierarchical space makes sense.
5. There are times when you may wish to optimize more than one metric. For example, we would like to optimize model accuracy, while minimizing model latency. Vizier can find the Pareto frontier, which presents tradeoffs for multiple metrics, allowing users to choose the appropriate tradeoff. Simple example: I want to make a more accurate model, but would like to minimize serving latency. I do not know ahead of time what’s the tradeoff between the two metrics. Vizier can be used to explore and plot a tradeoff curve, so users can select on the most appropriate one. For example, “a latency decrease of 200ms will only decrease accuracy by 0.5%”

Google Vizier is all yours with Vertex AI

Google published the Vizier research paper in 2017, sharing our work and use cases for black-box optimization—i.e. The process of finding the best settings for a bunch of parameters or knobs when you can’t peer inside a system to see how well the knobs are working. The paper discusses our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning that the service provides. Vizier has been essential to our progress with machine learning at Google, which is why we are so excited to make it available to you on Vertex AI.

Vizier has already tuned millions of ML models at Google, and its algorithms are continuously improved for faster convergence and handling of real-life edge cases. Vertex Vizier’s models are very well calibrated and are self-tuning (they adapt to user data), and offer unique power features, such as hierarchical search spaces and multi-objective optimization. We believe Vertex Vizier’s set of features is a unique capability to Google Cloud, and look forward to optimizing the quality of your models by automatically tuning hyperparameters for you.

To learn more about Vertex Vizier, check out these docs and if you are interested in what’s coming in machine learning over the next five years, tune in to our Applied ML Summit on June 10th, or watch the sessions on demand in your own time.

Welcome to a new way of exploring Google Cloud products. Finding your favorite products and discovering new ones requires a user interface that’s easy-to-use, clear, informative, and delightful. Google Cloud users have primarily used our side menu to navigate, but with almost one hundred products and growing, it’s safe to say our product list has outgrown the side menu. Over time, we have listened long and hard to feedback from Google Cloud users, who have highlighted challenges navigating the console to explore our products. We’ve heard over and over that it’s difficult and time consuming to scroll through the long list of products and remember what each one offers. 

That’s why we created a new All products page to help you easily navigate to your favorite Google Cloud products. This page showcases all of the Google Cloud products as well as our key partner products in one, easy-to-navigate place. With one click, you can discover the right product that is right for your solution.

Explore all Google Cloud products

The page is organized into different categories, including Management (ie. IAM, Billing), Compute, Storage, Operations, Security, CI/CD, Artificial Intelligence, Support, and more. Quickly jump to the category of interest through the panel on the left, or you can scroll the entire page. Then you can click each product name to navigate directly to the product homepage. Each product listing also includes a short and long description so you can quickly understand what a product does and whether it fits your needs. This lets you compare categories at-a-glance, saving you the hassle of digging up product overviews elsewhere. 

Under each product, you’ll also find a link to documentation and Quickstarts so you can understand it in more depth and try it out right away, removing the extra step of navigating to documentation in another tab.

Customize your navigation

To make navigation even easier, you can pin products directly from the All products page, and they will show up at the top of your side menu. You can also customize your navigation by reordering your pins in the side menu. That way, you can quickly access your most-used products directly from the side menu instead of scrolling through the panel or the All products page. 

Save time and get more done faster

With the new All products page you can save time scrolling and cut straight to the good stuff – finding your products, discovering new ones, learning, and getting hands on. Try it out for yourself by heading to the Google Cloud Console. Click the side panel and click “View All products,” or on the home dashboard you’ll see a call out to try out the All products page.

If you have any feedback about this new experience, I want to hear! Reach out to me on Twitter at @stephr_wong or on Linkedin at stephrwong.