Moving Flock Freight to Google Cloud for a more efficient, resilient and environmentally sustainable shipping supply chain
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Commercial trucks often travel partially empty because many shippers don’t have enough cargo to fill an entire container or trailer. Although offering available space to other shippers helps minimize carbon emissions and reduce operating costs, most trucking companies can’t efficiently schedule, track, or deliver multiple freight loads.
Companies have always struggled to ship over-the-road freight efficiently. However,recent economic events have created an unprecedented logistics and transportation crisis that continues to disrupt supply chains, delay deliveries, and significantly raise the price of basic goods. Since some stores can’t keep their shelves fully stocked, many people across the country are finding it more difficult than ever to buy the things they need at an affordable price.
Although exacerbated by the pandemic, many of these supply chain issues have existed for decades. That’s why, in 2015, Flock Freight was started with the mission of reducing waste and inefficiency from the supply chain by reimagining the way freight moves. First to market with advanced algorithms that enable pooling shipments at scale, we create a new standard of service for shippers, increase revenue for carriers and reduce the impact of carbon emissions through shared truckload (STL) service.
Our technology helps lower prices compared to full truckload (FTL) by enabling shippers to only pay for the space they need—and maintain full control over pickup and delivery dates. Flock Freight also optimizes travel routes to speed up deliveries compared to traditional less than truckload (LTL), while eliminating unnecessary shipping hub transfers to minimize damage to cargo.
Today, thousands of shippers and trucking companies across the U.S. use Flock Freight to schedule shared truckloads, lower shipping costs, quickly deliver and track goods, and reduce their carbon footprint by up to 40%. Flock Freight further offsets carbon emissions by buying carbon credits for every FlockDirect™ guaranteed shared truckload shipment—at no extra cost to shippers.
Moving Flock Freight to Google Cloud
We founded Flock Freight with a small team based in southern California. We soon realized we needed a more scalable and affordable technology stack to support our rapidly growing platform and team. After joining the Google for Startups Cloud Program and consulting with dedicated Google startup experts, we decided to move all our data and applications to Google Cloud.
The highly secure-by-design infrastructure of Google Cloud now enables thousands of Flock Freight customers to move their freight faster, cheaper, and with less damage than traditional shipping methods. Specifically, we rely on Google Kubernetes Engine (GKE) to support the combinatorial optimization and machine learning (ML) algorithms and services that identify, pool, and schedule shared truckloads. We also leverage GKE to rapidly develop, deploy, and manage new applications and services.
In addition, we leverage Cloud SQL to automate database provisioning, storage capacity management, and other time-consuming tasks. Cloud SQL easily integrates with existing apps and Google Cloud services such as GKE and Pub/Sub. Lastly, we use Compute Engine to create and run virtual machines, optimize resource utilization, and lower computing costs by up to 91%. These cost savings allow us to shift more resources to R&D and rapidly develop new solutions and services for our customers.
Building a greener, more resilient, and responsive supply chain
The Google for Startups Cloud Program and dedicated Google startup experts were instrumental in helping us manage cloud infrastructure cost and maintaining very high SLAs, helping Flock Freight to focus on developing a comprehensive shipping platform that powers shared truckloads and drives positive industry change.
We especially want to highlight the Google Cloud research credits we relied on to launch Flock Freight and make rapid progress toward transforming the shipping industry. To this day, we continue to work with Google Cloud Managed Services partner DoiT International International to further scale and optimize operations on Google Cloud.
We’re proud of the results we’re delivering for our customers. For example, a home improvement importer now enjoys faster, safer, and easier shipping with 99.9% damage-free service and a 97.5% on-time delivery rate. A packaging supplier continues to maintain a 99% on-time delivery streak and decrease carbon emissions by 37%, while a mineral water company consistently reduces delivery expenses upwards of 50%.
Nationwide demand for shared truckloads continues to increase as the shipping industry works to lower costs and alleviate supply chain disruptions. With the Flock Freight platform, companies are building a more sustainable and resilient supply chain by efficiently combining multiple shipments into shared truckloads.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
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Every business is a digital business. That’s what you’ll hear from technology folks these days. But, what exactly is a digital business? How does one define it?
Simply put, digital businesses are those that have thoroughly capitalized on the opportunity to connect people with technology. There are four parts to a digital business:
Real-time data and analytics: To stay relevant in the age of Big Data, businesses must analyze copious amounts of data to derive actionable insights—both from historical data, and in real time.
Fast, flexible application development: Rapid and continuous delivery of software to your stakeholders is no longer optional. Businesses need platforms for their applications strategy — from using container-based development tools to fully managed serverless platforms.
Secure, reliable infrastructure: How secure is your on-prem datacenter? What happens if it goes down? How many dedicated security engineers do you have on staff? What’s the cost of a system upgrade? What digital businesses need is a secure and reliable infrastructure that can power their applications.
Constant collaboration and productivity: Digital businesses are designed to keep teams seamlessly connected not only to each other, but also to the applications that keep the company running. This enables everything and everyone to work together, no matter where they sit—across the office or across the ocean.
Download this infographic to know more.
Earth Week: Google Cloud at the Heart of Sustainability
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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.
8 Must-Have Google Cloud Products for Startups
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Startups worldwide turn to Google Cloud tools to build fast on a strong and easy to use platform that helps them get to market and launch products faster, all while building on the cleanest cloud in the industry. Startups leverage Google Cloud and our Google for Startups Cloud Program to go from idea to IPO, and there are a variety of products on Google Cloud that can help them.
Here are the 8 top products that startups use on Google Cloud to innovate and grow:
Firebase for app development
Speed up innovation with Firebase, a mobile development platform that’s fully integrated with Google Cloud. Work in a simpler cloud environment, easily pull in products or services, and build your apps faster.
Cloud SQL for database needs
Build your startup’s foundation with Cloud SQL, a fully managed relational database solution that integrates with Google Cloud services. Create and connect to your first database in minutes and scale with a single API call.
AI and machine learning products
Solve tough problems with AI and machine learning products, built with the best of Google’s technology. Train deep learning and machine learning models cost-effectively so you can iterate and innovate faster.
BigQuery for data analytics
Drive agility with BigQuery, a serverless, cost-effective, multi-cloud data warehouse. Query streaming data in real time, predict business outcomes with built-in machine learning, and share analytics with just a few clicks.
Google Kubernetes Engine (GKE) for containers
Unlock faster, more secure app development with GKE, the most scalable Kubernetes platform. Streamline operations with release channels that fit your business needs and leave cluster monitoring to Google engineers.
Looker for data visualization
Get more from your data to keep moving ahead of the competition with Looker, a trusted business intelligence and data platform. Generate real-time reports and get insights at the right time with proactive alerts.
Cloud Run for serverless computing
Create scalable containerized apps in any programming language on Cloud Run, a fully managed compute platform. Pair it with container tools like Cloud Build and Docker, and only pay when your code is running.
Cloud Armor for security
Protect your startup from Web attacks with Cloud Armor, a leading Distributed Denial-of-Service (DDOS) defense service. Use it with an HTTP Load Balancer for Managed Instance Groups across regions to keep your workloads highly available and secure.
To learn more about products best-suited to the unique demands of startups, check out our startups solution page. Our team is looking forward to discussing how these products can help you. If you’re not already in the program, you can get started here.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Accelerating AI Inference at Scale: Introducing Google Cloud TPU v5e
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Google Cloud’s AI-optimized infrastructure makes it possible for businesses to train, fine-tune, and run inference on state-of-the-art AI models faster, at greater scale, and at lower cost. We are excited to announce the preview of inference on Cloud TPUs. The new Cloud TPU v5e enables high-performance and cost-effective inference for a broad range AI workloads, including the latest state-of-the-art large language models (LLMs) and generative AI models.
As new models are released and AI becomes more sophisticated, businesses require more powerful and cost efficient compute options. Google is an AI-first company, so our AI-optimized infrastructure is built to deliver the global scale and performance demanded by Google products like YouTube, Gmail, Google Maps, Google Play, and Android that serve billions of users — as well as our cloud customers.
LLM and generative AI breakthroughs require vast amounts of computation to train and serve AI models. We’ve custom-designed, built, and deployed Cloud TPU v5e to cost-efficiently meet this growing computational demand.
Cloud TPU v5e is a great choice for accelerating your AI inference workloads:
- Cost Efficient: Up to 2.5x more performance per dollar and up to 1.7x lower latency for inference compared to TPU v4.
- Scalable: Eight TPU shapes support the full range of LLM and generative AI model sizes, up to 2 trillion parameters.
- Versatile: Robust AI framework and orchestration support.
In this blog, we’ll dive deeper into how you can leverage TPU v5e effectively for AI inference.
Up to 2.5x more performance per dollar and up to 1.7x lower latency for inference
Each TPU v5e chip provides up to 393 trillion int8 operations per second (TOPS), allowing complex models to make fast predictions. A TPU v5e pod consists of 256 chips networked over ultra-fast links. Each TPU v5e pod delivers up to 100 quadrillion int8 operations per second, or 100 PetaOps, of compute power.
We optimized the Cloud TPU inference software stack to take full advantage of this powerful hardware. The inference stack leverages XLA, Google’s AI compiler, which generates highly-efficient code for TPUs to maximize performance and efficiency.
The combined hardware and software optimizations, including int8 quantization, enable Cloud TPU v5e to achieve up to 2.5x greater inference performance per dollar than Cloud TPU v4 on state-of-the-art LLM and generative AI models, including Llama 2, GPT-3, and Stable Diffusion 2.1:
Google Internal Data. August 2023. Normalized to single-chip throughput. Precision: Llama 2 7B, 13B, 70B, GPT-J 6B: int8; GPT-J 175B, Stable Diffusion 2.1: bf16.
On latency, Cloud TPU v5e achieves up to 1.7x speedup compared to TPU v4:
Google Internal Data. August 2023. Precision: Llama 2 7B, 13B and 70B: int8; GPT-3 175B: bf16.
Google Cloud customers have been running inference on Cloud TPU v5e, and some have seen even greater speedups on their particular workloads.
AssemblyAI offers dozens of AI models to their customers for speech recognition and understanding with over 25 million inference calls on a daily basis.
“Cloud TPU v5e consistently delivered up to 4X greater performance per dollar than comparable solutions in the market for running inference on our production model. The Google Cloud software stack is optimized for peak performance and efficiency, taking full advantage of the TPU v5e hardware that was purpose-built for accelerating the most advanced AI and ML models. This powerful and versatile combination of hardware and software dramatically accelerated our time to solution: instead of spending weeks hand-tuning custom kernels, within hours we optimized our model to meet and exceed our inference performance targets.” – Domenic Donato, VP of Technology, AssemblyAI
Scale to the full range of LLM and Generative AI model sizes
LLMs and generative AI models continue to grow in size and computational cost. The largest models require the combined compute and memory of hundreds of hardware accelerators. Cloud TPU v5e enables inference for a wide range of model sizes. A single v5e chip can run models with up to 13B parameters. From there, you can scale up to hundreds of chips and run models with up to 2 trillion parameters.
Google Internal Data. August 2023. Batch size = 1. Multi-head attention based decoder only language models: prefix length = 2048, decode steps = 256, beam size = 32 for sampling.
Gridspace leverages Google Cloud TPU infrastructure to power its full-stack conversational AI platform – building and integrating real-time conversational ASR, LLMs, semantic search, and neural TTS.
“We’re a huge fan of Google Cloud TPUs. Our benchmarks are demonstrating a 5X increase in the speed of AI models when training and running on Google Cloud TPU v5e. We are also seeing a 6x improvement in the scale of our inference metrics. We’ve scaled our AI models to billions of conversations per year across financial services, capital markets, and healthcare with Google Cloud’s AI infrastructure. Our Grace bots are powered by models trained using Cloud TPUs and served at scale on GKE with support for PCI, HITRUST, and SOC 2 compliance.” – Wonkyum Lee, Head of Machine Learning, Gridspace
Robust AI framework and orchestration support
Leading AI frameworks, including PyTorch, JAX, and TensorFlow, provide robust support for inference on Cloud TPU v5e. This means you can now train and serve models end-to-end on Cloud TPUs: what you train is what you serve.
Google Cloud offers you many choices to run inference on Cloud TPUs easily and reliably. From GKE and Vertex AI, to popular open-source frameworks such as Ray and Slurm, you can leverage Google Cloud TPUs in your preferred way to fit your development process.
Try Cloud TPU v5e for inference today
Cloud TPU v5e provides a high-performance, cost-efficient, scalable, and reliable inference platform for LLMs and generative AI models. Leading AI companies are leveraging the power of Cloud TPU v5e to serve AI models at scale:
To get started with inference on Cloud TPU, reach out to your Google Cloud account manager or contact Google Cloud sales.
Data-first Digitization Helps Leverage the Cloud for Your Mainframe Assets
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For many enterprises, the venerable mainframe is home to decades’ worth of data about the company’s customers, processes and operations. And it goes without saying that the business would like access to that mainframe data — to report on it, to analyze it with big data analysis tools, or to use it as the basis of new machine learning and artificial intelligence initiatives.
At Google Cloud, we are eager to work with organizations to help them transform their mainframe assets for the cloud era. Of course, we can help them modernize their mainframe applications by migrating them to the cloud. At the same time, working with partners and customers, we’ve developed another, more lightweight approach that can help them start to leverage the cloud for their mainframe assets much more quickly than performing a full-fledged migration. We call this approach data-first digitization.
In this rapidly evolving digital ecosystem, it’s imperative to understand the difference between ‘modernization’ and ‘digitization.’ With modernization you start with the current state and look forward, and rely on mainframe application migration approaches such as rehosting (emulation), refactoring (automated code transformation), reengineering — or simply replacing a custom application with a commercial package. With digitization, you start with the future state that you want to achieve, and work back to what is required to get there.
This data-first digitization approach includes a mainframe data-first integration framework comprising in-house and partner products and tools to migrate heterogeneous data sources from the mainframe to Google Cloud Storage. Once mainframe data has been copied to Cloud Storage, it can then be integrated and leveraged by Google Cloud tools such as BigQuery, AI and machine learning prodcuts and Smart and Stream analytics platforms. The integration framework covers both bulk batch data transfers and real-time data replication (change data capture).
Data-first digitization is based on the tenet that ‘applications are transient, data is permanent.’ By bringing data first to Google Cloud instead of traditional ways of modernizing applications (for example, with Gartner’s 7 options to Modernize), this allows organizations to leapfrog to new business models, use cases and innovative ways to serve end customers. For example:
- Making decisions with smart and stream analytics platforms and AI/ML engines. These tools need data to make decisions. Google is a pioneer in extracting information and value from the raw structured and unstructured data, and this approach opens up mainframe data for use by BigQuery and AI/ML models.
- Building new reporting applications. With access to mainframe data, you can use Google cloud products like Looker and Appsheet to build net-new reporting applications, expediting the process of retiring mainframe reporting applications, and accelerating your overall transformation.
In our experience, taking a data-first digitization approach to your mainframe offers a number of benefits:
- Faster time-to-business: Because data-first modernization is built on existing products, the implementation cycle is much shorter.
- Less capital investment: You spend your time integrating products, not developing applications.
- Minimized risk: Data-first integrates with existing, proven and reliable Google Cloud products.
- Faster overall mainframe transformation: When you shift your modernization center of gravity from the application to the data, you look at mainframe applications from a business perspective instead of just “keeping the lights on.” As a result, only the most business-critical applications are modernized and many support applications can be decommissioned, accelerating your transformation journey.
Taking a data-first approach to digitization is still relatively new, but we’re heartened by customers’ early successes. Watch this space for additional insights, reference architectures and technical white papers around data-first. And if you think this approach may be right for you, reach out to mainframe@google.com.
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