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.

Digital technology offers increasing flexibility and choice to consumers. As a result, the retail industry is dramatically shifting toward more tailored and personalized experiences for shoppers, and businesses are rethinking how they deliver value to customers.

This couldn’t be more true for the beauty retailing industry where leading companies are turning to digital technology to create customized shopping experiences.

At Google Cloud, we’re particularly excited about our work with Ulta Beauty, the largest beauty retailer in the United States with more than 1196 stores in all 50 states, and how the company is using Google Cloud technology solutions to power personalization and redefine beauty retailing.

Established in 1990, Ulta Beauty has had incredible success as a company, and as customers become more discerning and curious about their purchases, the company is finding new ways to meet their changing needs.

Recently, leaders at Ulta Beauty recognized a huge opportunity to complement and enhance the shopping experience by helping beauty enthusiasts navigate through more than 500 brands and 25,000 products carried in their stores and online channel.

They decided to leverage the data from Ulta Beauty’s successful Ultamate Rewards loyalty program to create and offer more unique and personalized user experiences.

With more than 30 million members generating data through sales, transactions, product reviews, and social media engagement, Ulta Beauty’s Loyalty Program creates a comprehensive data set, and the company sought the right technology partner to help organize, analyze and transform that data into valuable insights for its customers.

Ulta Beauty’s leaders knew they had an opportunity to leverage data analytics and machine learning to reach customers in new ways, enhance the guest experience, and continue to grow their active loyalty member base. After considering a number of cloud providers, they chose to expand their existing partnership with Google Cloud.

“Google Cloud listened to our needs and worked in tandem with our engineering team to address our challenges,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “The ease of working with the Google Cloud team and their breadth of experience made the decision a no-brainer, laying the foundation for a great partnership.”  

In 2019, Ulta Beauty announced it was working with Google Cloud Platform to unify and organize its data, using:

• BigQuery to perform data analysis and generate dynamic content, personalized product recommendations, and event-based messages for customers.
• Cloud Storage to provide highly available, secure, resilient and cost-effective access to data across the entire enterprise.
• Compute Engine for the high-performance scalability needed to grow with customer demand while painlessly migrating existing applications to the cloud.
• Anthos to build a hybrid cloud foundation that allows their applications to take advantage of all this data, combining the power and flexibility of GKE with the ability to leverage their existing investment in secure infrastructure on-premises.

Our partnership with Ulta Beauty has enabled increased engagement with customers in store and online, and the creation of new tools and capabilities, including a new Virtual Beauty Advisor tool to deliver tailored recommendations and help shoppers choose the right products, and a Customer Conversation Platform that’s enabling deeper connections with guests, ultimately driving customer loyalty.

“It’s been a really efficient process so far due in part to the ease of working with the Google team,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “They’re experienced, approachable, and their can-do style makes for a great partnership. They listened to our needs and worked in tandem with our engineering team, figuring things out, and getting it done.”

Today we are excited to announce that Google and the Istio Steering Committee have submitted the Istio project for consideration as an incubating project within the Cloud Native Computing Foundation (CNCF). This is a significant milestone for Istio and its community, and we are thrilled to reach this next step in the evolution of the project.

Google and Istio

Google originated the Istio project, which alongside Kubernetes and Knative, is a critical part of cloud-native infrastructure. The Istio project has found success and maturity in its current model — being adopted by hundreds of organizations and bringing in over 4,000 developers for IstioCon.

For over 20 years, Google has helped shape the future of computing with its open source contributions and has invested deeply to unlock innovation for our customers. Istio extends Kubernetes to establish a programmable, application-aware network using the Envoy service proxy. Istio works with both Kubernetes-based and traditional workloads, and brings standard, universal traffic management, telemetry, and security to complex deployments. Finding a home in the CNCF brings Istio closer to the cloud-native ecosystem and will foster continuing open innovation.

The Istio journey

We started to develop Istio in partnership with teams at IBM and Lyft in 2016 based on patterns that were being used to connect Google production applications. Google’s security focus complemented the traffic management focus of an open-source project published by IBM, and those two teams decided to collaborate on Istio directly. Istio was launched “fully formed” in May 2017, with the v0.1 release featuring sidecar-powered traffic control, observability and policy features — the things that today define a service mesh. Istio has been open source from the beginning, and has a governance structure that promotes continuous contribution and project engagement.

By the release of v0.3 a few months later there were users trusting it in production, seeing immediate benefits from its powerful features. The project reached v1.0 in July 2018, and was being used at scale by eBay and The Weather Company. Google led a major re-architecture with the release of v1.5, unifying the control plane into a single service. This change, based on user feedback, reduced administrative overhead, as was later written about in the IEEE Software journal. We also greatly simplified extensibility of the mesh by building support for WebAssembly plugins into Envoy.

Istio is now offered as a managed or hosted service by over 20 providers, including Anthos Service Mesh, a suite of tools that helps you monitor and manage a reliable service mesh on-premises or on Google Cloud.

The future of Istio as a project in the CNCF

At Google, we believe that using open source comes with a responsibility to contribute, sustain, and improve the ecosystem, and we are committed to improving critical cloud-native projects on behalf of our customers and the community at large. Google has made over half of all contributions to Istio and two-thirds of the commits, as measured by the CNCF DevStats.1 After deciding to adopt Envoy for Istio, Google rose to be Envoy’s number-one contributor.2

Istio is the last major component of organizations’ Kubernetes ecosystem to sit outside of the CNCF, and its APIs are well-aligned to Kubernetes. On the heels of our recent donation of Knative to the CNCF, acceptance of Istio will complete our cloud-native stack under the auspices of the foundation, and bring Istio closer to the Kubernetes project. Joining the CNCF also makes it easier for contributors and customers to demonstrate support and governance in line with the standards of other critical cloud-native projects, and we are excited to help support the growth and adoption of the project as a result.

Istio is key to the future of Google Cloud and if the project is accepted, Google will continue to strategically invest in Istio as a key maintainer and through ongoing investment in engineering for upstream contributions.

^{1. https://istio.teststats.cncf.io/d/5/companies-table?orgId=1
2. https://envoy.devstats.cncf.io/d/5/companies-table?orgId=1}