Today’s Google Doodle reminds us of the enormous changes our planet is experiencing due to climate change. Everyone, from businesses to governments to technologists, has the opportunity to meet this challenge — transforming themselves and their organizations to be more sustainable. For this Earth Day 2022, and indeed Earth Week, Google Cloud celebrates the organizations and individuals who are fighting climate change with innovative technology. We don’t want you to miss a thing, so here’s a recap of all our news in one handy location.

We asked global CEOs: what is it going to take to make progress on sustainability in your org?
In a survey of 1,500 CXOs across 16 countries, many executives say they are willing to do what it takes to have more sustainable practices. But despite their ambition, real measures of impact are lacking. To see what will change that, check out the blog.

We announced a new innovation challenge supporting climate science and research…
Our blog on Monday announced the Climate Innovation Challenge Research Credits program, to support researchers as they work to better understand climate change, increase climate resilience and develop new, promising solutions to urgent climate challenges. You can apply for research credits here.

…and shared stories of researchers making a difference
We interviewed Dr. Richard Fernandes from Natural Resources Canada, who built the LEAF toolbox that maps and assesses vegetation with satellite data from Google Earth Engine. You can read our Q&A here.

Canada has approximately 10 million square kilometers of land and the annual data volume of these maps is equivalent to streaming HD movies for over 750 hours non-stop. Cloud computing allows us to manage all this data in a useful and accessible way.

Dr. Fernandes, Research Scientist

We also published a story about the U.S. Department of Agriculture’s Forest Service, and how they use Google Cloud processing and analysis tools to help sustainably manage 193 million acres of land.

We turned the lights on at new clean energy projects in four countries…
We shared details of our battery project in Belgium, solar projects in Denmark, and wind projects in Chile and Finland. Our battery project in Belgium is the first of its kind, enabling us to switch from diesel generators to a cleaner backup solution that will keep the internet up and running in the event of a power disruption. These will all help us continue to operate the cleanest cloud in the industry.

…and made it easier to learn how to build applications more sustainably
We launched a new lab that walks users through our Carbon Sense suite of products. From using our region picker app to make low-carbon architecture decisions, to analyzing the carbon footprint of your Google Cloud app with Carbon Footprint, we’re building sustainability into the tools you use every day. You can also find Carbon Footprint training in the new Data Warehouse Cloud On-Board.

We formed an ecosystem of partners to help accelerate sustainability projects…
The Google Cloud partner ecosystem is critical to helping our customers act sustainably today. A new whitepaper produced in partnership with Enterprise Strategy Group shares real-world solutions that could make an immediate impact — not in the next decade, but right now.

…and shared stories of innovative startups changing the game with Google Cloud.
Take Enexor and its partners, who are producing clean and sustainable energy from discarded plastics and agro-waste. The blog from Lee Jestings, Enexor Founder & CEO, shares how Google for Startups got them started, and which Google Cloud tools help them build predictive models. Check out their story.

Or Nuuly, the rental and resale business created by the URBN portfolio, which also includes Urban Outfitters, Anthropologie, and Free People. In the blog you can read how Nuuly is using technology to provide a sustainable experience to employees and customers — from upcycling clothing, to recyclable and reusable packaging.

Whether you’re a startup, scientist, executive or developer, at Google Cloud we’ll continue to work hard to help make your digital transformation a sustainable one.

Learn more about our sustainability work here, and don’t miss the inaugural Cloud Sustainability Summit this June. Register now.

High Performance Computing (HPC) is prevalent today across many industries, including financial services, life sciences, higher education research, manufacturing, and energy. More and more businesses are deploying HPC workloads in the cloud to take advantage of its elasticity, scalability, and availability. Job schedulers are critical for HPC applications given the nature of these workloads that create, process, and tear down thousands, and sometimes millions, of vCPU and network resources with TB to PB of storage capacity. Job scheduling tools lead to improved operational efficiency and a degree of certainty that a particular HPC job, which can run for hours to weeks, will complete successfully. 

IBM Spectrum LSF is used extensively in the manufacturing and semiconductor industry to manage Electronic Design Automation (EDA) workloads. Dynamically running workloads on-premises and in the cloud, also known as cloud bursting, is becoming a more common practice to address capacity and provisioning time constraints within data centers and enable enterprises to take advantage of virtually unlimited resources. 

However, the challenge with cloud bursting is integrating and maintaining operational consistency across on-premises and cloud environments. IBM Spectrum LSF, in combination with Google Cloud, addresses this problem head on. 

Google Cloud is excited to announce, in collaboration with IBM, enhanced capabilities to IBM Spectrum LSF that enables organizations to integrate their on-premises job scheduling scripts with resources deployed in Google Cloud. Customers are now able to fully leverage Google Cloud’s highly scalable and secure Compute Engine, networking and storage infrastructure. 

The LSF-Google Cloud resource connector patch supports key Google Cloud differentiators including Local SSDs, GCE instance templates, Preemptible VMs, and more: 

• Bulk API support– Deploy large fleets of VM instances in a matter of seconds.
• Instance Templates – Simplify VM configuration by creating reusable templates. 
• All Machine Types – Supports all GCE VM families and machine types, including Custom Machine Types.
• GPUs – Attach up to 16 GPUs per instance, including the largest A2 instances with up to 16 NVIDIA A100 GPUs
• Preemptible VMs – Preemptible VMs are provisioned from excess Compute Engine capacity and is a significant way to save money on GCE resources.
• Local SSD – Attach up to 9TB of NVMe SSD per instance
• Hyperthreading – Supports the “threads-per-core” option in GCE, which allows per-VM hypervisor level Hyperthread configuration (when supported by Instance Templates)
• Images – Supports custom disk images, including full support for Windows, for all attached Persistent Disks
• Placement Policies – Control where the instances are physically located relative to each other within a zone for improved low-latency performance
• Labels – Supports GCE Labels, which can be used for management of firewall rules, tracking billing, etc
• Minimum CPU Platform – Supports the ability to specify a minimum CPU Platform for your Virtual Machines.

Getting Started

This improvement to the IBM LSF Resource Connector was developed by IBM in coordination with Google. Find out more about the new supported features and their operation in the official IBM Spectrum LSF Resource Connector Documentation. You can also find additional documentation and download the software in the IBM Spectrum Computing Community. If you have further questions, you can contact IBM, or Google Cloud Sales.

Special thanks to Annie Ma-Weaver, Mark Mims, and Wyatt Gorman for their contributions.

Building a strong brand in today’s hyper-competitive business environment takes vision. It also requires a flexible, easily managed approach to digital asset management (DAM), so marketing professionals and other stakeholders can easily share, store, track, and manipulate assets to build the brand.

Many of today’s leading companies, including JetBlue, Slack, TripAdvisor, Lyft, and HealthONE, rely on Brandfolder to deliver consistent, organized, and efficient brand experiences. Brandfolder provides an easy-to-use platform that can scale across an entire company with little end-user training, empowering customers to distribute digital assets wherever they are needed. Customers also gain much greater insight into how those assets are used, and how to use them more effectively in marketing campaigns and brand messaging.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform.”

—Ajay Rajasekharan, Head of Data Science, Brandfolder

Brandfolder is constantly advancing its development efforts to introduce new data-driven features without complicating the user experience. Big data, artificial intelligence (AI), and machine learning (ML) are key to meeting customers’ unique business needs, and essential for Brandfolder to compete in the fast-moving DAM industry. To enhance these capabilities, Brandfolder sought a public cloud provider that could help it scale its data pipeline cost effectively while providing access to advanced AI technologies.

After graduating from the Techstars startup accelerator program in 2013, Brandfolder tried two other cloud providers before standardizing on Google Cloud Platform (GCP).

“We saw a difference with Google Cloud from the very beginning because the interactions felt like a strategic relationship,” says Jim Hanifen, Head of Product at Brandfolder. “Google gave us startup credits and a lot of face-to-face support, which we hadn’t experienced with other cloud providers. We decided to move our entire infrastructure to Google Cloud Platform.”

Building an ML platform for brand intelligence

After performing an initial lift-and-shift migration of virtual machines (VMs) onto Compute Engine, Brandfolder built an ML platform using GCP managed services to seamlessly deliver its data products. The platform leverages Cloud SQL, Cloud Storage as the data lake, Cloud Dataproc for cloud-native Apache Spark computing clusters, Cloud Composer as the batch job scheduler, Cloud Pub/Sub as the backbone data pipeline, Container Registry to store Docker images, and Google Kubernetes Engine (GKE) as the application orchestrator. Cloud Dataflow brings data into the data lake and into BigQuery for analysis.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform,” says Ajay Rajasekharan, Head of Data Science at Brandfolder, who describes the architecture in a detailed blog. “We simply ingest raw application and event data on one end and output an ML service on the other.”

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost. We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

—Brett Nekolny, Head of Engineering, Brandfolder

For many general use cases, Brandfolder does not need to build custom ML models, and instead relies on pre-trained API models from GCP. For example, it uses Vision API and Video Intelligence API to auto-tag creative assets on import to enable fast, intuitive searches across images and videos. When more product- and brand-specific modeling is required to address unique customer use cases, Brandfolder builds and trains custom ML models using its GCP pipeline or Cloud AutoML, a suite of products built on Google transfer learning and neural architecture search technology. For example, if a Brandfolder customer makes different types of grills, Brandfolder can use AutoML Vision to train a model to recognize the different grills.

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost,” explains Brett Nekolny, Head of Engineering at Brandfolder. “We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

Industry-leading security and performance

Google Cloud’s security model helps Brandfolder give existing and prospective customers peace of mind that their data will be protected. Cloud Identity & Access Management (Cloud IAM) provides enterprise-grade access control, while Cloud Identity-Aware Proxy (Cloud IAP) enables remote users to work more securely without the hassles of a VPN client. GCP also isolates cloud resources into projects, making it easy to assign permissions and keep data and VMs organized and segregated.

“With Google Cloud, everything begins and ends with security, which makes things very easy for us,” says Jim. “If we’re under a security review, we can submit a Google security white paper. If a potential customer has security concerns, we tell them we are hosted on GCP, and those concerns go away.”

To give customers even better application performance for accessing their brand assets, Brandfolder uses Cloud Memorystore, an in-memory data store service for Redis, to cache data and provide sub-millisecond data access for production applications.

“It was much easier for us to use Cloud Memorystore versus running Redis on our compute instances,” says Brett. “The high availability, replication across zones, and automatic failover with no data loss are big for us.”

Global private network interconnects between Google Cloud and the Fastly content delivery network (CDN) dramatically reduce latency, allowing Brandfolder’s customers to deliver and update even very large creative assets quickly around the world.

“What’s beautiful about the relationship between Google and Fastly is that if one of our customers uploads a new version of an asset, we can propagate that out to Fastly, and the new version will automatically show up in all the places where it’s referenced,” says Brett.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter. Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

—Jim Hanifen, Head of Product, Brandfolder

Improving employee and customer productivity

Brandfolder also uses Google solutions for real-time collaboration and productivity, using G Suite to connect employees with intuitive, cloud-based apps. Teams use Gmail, Calendar, Docs, Drive, Sheets, Slides, and Hangouts Meet every day to move the business forward. Many of Brandfolder’s customers are also G Suite users, and Brandfolder offers a plug-in that allows them to view their creative assets inside of Docs and pull images in as needed. Customers can also log into Brandfolder with their G Suite credentials, making the solution even easier to use.

“We’ve been using G Suite since the beginning, and it’s helped us collaborate efficiently to build a successful, growing company,” says Jim. “Our teams expect to have that kind of close collaboration, and everyone here enjoys the G Suite experience.”

Driving 99 percent annual business growth

With automated tagging and other innovative AI-based features, Brandfolder is helping customers locate and distribute assets faster. As a result, Brandfolder is building customer loyalty and increasing sales, growing its business by 99 percent year-over-year. Since moving to GCP, Brandfolder has been able to scale its analytics and data pipeline 50x without a corresponding increase in costs and has not had to expand its development team.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter,” says Jim. “Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”