From electrifying transportation to shifting the grid to renewable energy, environmental sustainability is one of the greatest challenges of our generation. A critical but often forgotten goal is the development of sustainable agricultural practices, especially given increasing water shortages and soil degradation around the world.

Lucent Bio was born to solve some of these threats to humanity’s ability to feed itself. It delivers crop nutrition solutions that accelerate the transition to sustainable agriculture. Lucent Bio developed novel technology for bioactive crop nutrition products, including its flagship product—Soileos®—which boosts nutrient density in crops, regenerates soil, avoids polluting agro-ecosystems, and enables the circular economy. Soileos is a plant-based product that is made by upcycling food processing co-products such as lentil and pea hulls into bioactive nutrients that then create the next harvest.

As a start-up, Lucent Bio has used Google Workspace since day one to drive collaboration, streamline operations, and scale. As an organization, it is also closely aligned with Google’s sustainability goals. Google is carbon-neutral for its operations and has made a public commitment with detailed plans to run on carbon-free energy by 2030, meaning clean power every hour of every day.

Google Workspace has been an invaluable resource to fuel collaboration between the engineers at the Lucent Bio pilot plant, the scientists at the research lab and greenhouse, and on-field agronomists as they conduct trials on Soileos across North America. Without the use of Google Workspace during the COVID 19 pandemic, Lucent Bio would not have been able to achieve their current scale.

“In just 18 months, Lucent Bio has been able to scale manufacturing from 1 kg to 1,000 kg of product per day. This year, we’re poised to take the next big step to 20,000 kg per day. During this scale-up process, we went through several iterations of improvements, resulting in a completely zero-waste manufacturing process—a hugely difficult but impressive achievement in line with our mission to accelerate the transformation of agriculture to sustainability. Every step of the way, Google Workspace has been an essential part of our innovation and growth strategy.”
Michael Riedijk, CEO Lucent Bio

Google Workspace runs on the cleanest cloud in the industry. It helps teams of all sizes, and across all industries, connect, create, and collaborate from anywhere. Workspace recently launched AI-generated summaries in Spaces, which help distributed teams stay focused while quickly catching up on chat messages they might have missed. Innovations like these keep the scientists at Lucent Bio collaborating as they build a more sustainable future for agriculture.

You can find more about Google’s commitment to sustainability here, including our goal to become not just carbon-neutral, but carbon-free by 2030, efforts to combat deforestation, and how we’re supporting clean energy.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

At Google, we believe that when it comes to solving a problem as big and urgent as climate change, we get more done when we collaborate. From beekeepers in Germany to urban foresters in Los Angeles, we support the work of nonprofits, scientists, and organizations that are working to mitigate the impact of climate change globally, and increase communities’ resilience to its effects.

To this end, we are proud to launch the Climate Innovation Challenge, which will provide Google Cloud research credits to advance a better understanding of climate resilience and promising solutions to address urgent climate challenges. I’m excited about this launch. We need to get smarter about the inevitable impact of our changing climate and how it will reshape our lives, supply chains, and business. Sustainability is a business-critical agenda, and we need intelligent technologies, leadership, and collaboration to drive industry transformations and reach a net-zero world.

The innovation and scale required to solve the toughest climate challenges will come from technology. At Google Cloud, we’re working across industries to increase climate resilience, applying cloud technology to help solve key challenges in the fight against climate change. Nonprofits, scientists, and organizations will be key in developing new research and innovations that will help us better understand how we can accelerate action on climate.

Through the new program, individual climate researchers in higher education and not-for-profit research organizations can apply for Google Cloud credit grants of up to $100,000 to accelerate their projects with Google’s state-of-the-art, data analytics and artificial intelligence (AI) cloud services, from Google Earth Engine (EE) to Google Public Datasets like the one from the National Oceanographic and Atmospheric Administration (NOAA). We will work with specialist partners in environmental organizations, agriculture, and carbon reduction to help evaluate proposals and select participants. Our first partner is the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Alongside the cloud credits, we will also provide researchers with access to technical training and mentoring, to help jumpstart their work.

From idea to insight to impact

In 2021, researchers at 500 universities in 47 countries received Google Cloud research credit grants. Others received funding through the Google Cloud Research Innovators Program, which promotes collaboration among a global cohort of scientists and provides them with professional opportunities and technical expertise. Here are some of the Research Innovators who have already advanced their climate research with Google Cloud:

• At CalTech Tapio Schneider and his team built Climate Machine, a next-generation open-source Earth System Model that will integrate more earth and atmospheric data than ever before.
• At UCLA, Bo Zhou uses EE for remote sensor modeling to help bureaus of land management make conservation decisions.
• At the University of New England, James Brinkhoff conducts spatial and temporal analysis for agriculture crop modeling and water use with data from EE.
• At Natural Resources Canada (NRCan), Richard Fernandes is developing the LEAF toolbox to map and assess vegetation with satellite data from EE.
• At the University of Toronto, Yuhong He uses EE to map changes occurring in natural and managed ecosystems systems using remote sensing, machine learning, and ecosystem modelings.
• At UCLA, Henry Houskeeper uses machine learning with EE’s satellite imagery to automate detection of kelp forests.
• At the University of Hawaii at Manoa, Jonghyun Lee conducts numerical modeling for water resources with Google Colab.
• At Technical University in Dublin, Santos Fernández Noguerol runs functions to collect weather data from governmental agencies, then automatically stores them in Google Cloud Storage buckets for future spatial analysis.
• At the University of Colorado at Denver, Farnoush Banaei-Kashani conducts data science projects with applications for Intelligent Transportation and Earth Sciences.

To apply for a Climate Innovation Challenge grant, click here and include “Climate Innovation Challenge” as the first line of your proposal. We will announce additional focus areas, partnerships, and recipients throughout the year. Click here to learn more about Google Cloud sustainability.

At Google Cloud, we believe you get most benefits from the cloud when you scale infrastructure based on changing demand. Compute Engine allows you to configure autoscaling to save costs during periods of low demand, and add capacity to support peak loads. 

When you use a managed instance group (MIG), you can have an autoscaler automatically create or delete virtual machine (VM) instances based on increases or decreases in load. However, if your application takes several minutes to initialize, creating VMs in response to growing load might not increase your application’s capacity quickly enough. For example, if there’s a large increase in load (like when users first wake up in the morning), some users might experience delays while your application is initializing on new instances.

A good way to solve this problem would be to create VMs ahead of demand so that your application has enough time to initialize beforehand. This requires knowing upcoming demand. If only we could predict the future… Well, now we can!

Introducing predictive autoscaling

Predictive autoscaling uses Google Cloud’s machine learning capabilities to forecast capacity needs. It creates VMs ahead of growing demand allowing enough time for your application to initialize.

How does it work?

Predictive autoscaling uses your instance group’s CPU history to forecast future load and calculate how many VMs are needed to meet your target CPU utilization. Our machine learning adjusts the forecast based on recurring load patterns for each MIG. 

You can specify how far in advance you want autoscaler to create new VMs by configuring the application initialization period. For example, if your app takes 5 minutes to initialize, autoscaler will create new instances 5 minutes ahead of the anticipated load increase. This allows you to keep your CPU utilization within the target and keep your application responsive even when there’s high growth in demand. 

Many of our customers have different capacity needs during different times of the day or different days of the week. Our forecasting model understands weekly and daily patterns to cover for these differences. For example, if your app usually needs less capacity on the weekend our forecast will capture that. Or, if you have higher capacity needs during working hours, we also have you covered.

Why should you try it?

Predictive autoscaling continuously adapts forecasted capacity to best match upcoming demand. Autoscaler checks the forecast several times per minute and creates or deletes VMs to match its prediction. The forecast itself is updated every few minutes to match recent load trends so if your growth rate is higher or lower than usual we will adjust the forecast accordingly. This gives you capacity needed to cover peak load while saving on cost when demand goes down. 

You can start using predictive autoscaling without worry as it’s fully compatible with the current autoscaler. Autoscaler will calculate enough VMs to cover both forecasted as well as real-time CPU load—whichever is higher. This works with other autoscaling features as well: you can scale based on schedule, your Load Balancer request target or Cloud Monitoring metrics. Autoscaler provides enough capacity to all of your configurations by taking the highest number of VMs needed to meet all your targets.

Getting started

You can enable predictive autoscaling in the Google Cloud Console. Select an autoscaled MIG from the instance groups page and click Edit group. Change predictive autoscaling configuration from Off to Optimize for availability.

To better understand whether predictive autoscaling is good for your application, click the link See if predictive autoscaling can optimize your availability. This will show you a comparison of the last seven days with your current autoscaling configuration vs. with predictive autoscaling enabled.

In the above chart, 

• Average VM minutes overloaded per day shows how often your VMs exceed your CPU utilization target. This happens when demand is higher than available capacity. Predictive autoscaling can reduce this by starting VMs ahead of anticipated load. 
• Average VMs per day is a proxy for cost. This shows how much additional VM capacity you need to keep your CPU utilization within the target you have set. You can optimize your cost by adjusting Minimum instances andCPU utilization as explained below. 

Optimizing your configuration

Make sure your Cool down period reflects how long it takes for your application to initialize from VM boot time until it’s ready to serve the load. Predictive autoscaling will use this value to start VMs ahead of forecasted load. If you set it to 10 minutes (600 seconds) your VMs will start 10 minutes before the load is expected to increase.

Review your autoscaling CPU utilization target and Minimum number of instances. With predictive autoscaling you no longer need a buffer to compensate for the time it takes for a VM to start. If your application works best at 70% CPU utilization you don’t need to set target to a much lower value as predictive autoscaling will start VMs ahead of usual load. A higher CPU utilization and lower Minimum number of instances allows you to reduce the cost as you don’t need to pay for additional capacity to prepare for growing demand.

Try predictive autoscaling today

Predictive autoscaling is generally available across all Google Cloud regions. For more information on how to configure, simulate and monitor predictive autoscaling, consult the documentation.