In the competitive world of consumer goods, sustainability matters more than ever. In recent Google survey, 82% of consumers said sustainability is more top of mind now than it was before COVID-19¹ and 78% said that big businesses have a role to play in helping to fight climate change.² As well as delivering on customers’ heightened expectations, sustainable business practices can help organizations reduce waste and lower operational costs and even help attract top talent.

Not only is sustainability good for business performance and the bottom line, it’s great for the planet, too. And it’s something we care deeply about at Google. Since our earliest days, we have focused on developing services that significantly improve the lives of billions of people while operating our business in an environmentally sustainable way. In 2007, we were the first major company to become carbon neutral, and in 2017 we were the first to achieve 100% renewable energy. Today, we proudly operate the cleanest cloud in the industry. We’ve also been powering humanitarian, scientific, and environmental initiatives studying geospatial information and making it available for analysis via Google Earth Engine. 

Powering the future of responsible sourcing 

How does all this relate to CPG supply chains? The world relies on raw materials like palm oil, soy, and cocoa to produce the items we consume every day—such as coffee, chocolate, frozen foods, shampoo, toothpaste, cosmetics, and even household cleaning products. Yet as demand for these materials continues to grow, forests are under threat. Change is needed.

As sustainability moves further into the spotlight, in-demand crops face increased supplier scrutiny. Environmentally conscious companies want to know what percentage of their raw materials is sourced from deforestation-free suppliers—and how they can improve that number. Until recently, many CPG brands have found it hard to get real-time, reliable visibility into operations at a local supplier level, globally. 

Google Cloud, in partnership with NGIS, is helping brands gain a deeper understanding of raw material sourcing practices across supplier networks, so they can improve supplier performance and compliance in the fight against deforestation. The TraceMark solution, developed by NGIS, uses Google Earth Engine and BigQuery to analyze and visualize how suppliers behave over time. 

Google Earth Engine is the world’s largest archive of open Earth data. It combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities to help people detect changes, map trends, and quantify differences on the Earth’s surface.With Earth Engine, organizations can analyze the potential social, and environmental impacts of the decision they make. 

Together, TraceMark, Google Earth Engine and BigQuery help companies visualize, monitor, and measure the impact of suppliers’ farming practices. The process starts with Earth Engine, which aggregates and harmonizes planetary data into images that it has been collecting for decades. Then, boundary maps for different suppliers are created and NGIS applies climate data science and machine learning to turn pixels into insights. Finally, BigQuery and Vertex AI -Google Cloud’s unified artificial intelligence platform are used to produce supplier scoring, which is integrated into downstream ERP and procurement systems to support more informed decision-making.

These efforts support the responsible sourcing of raw materials by making supply chains more agile, transparent and traceable.

“NGIS is thrilled to work with Google Cloud and its partners to enable business accountability for sustainable practices at all levels of their supply chain,” said Nathan Eaton, Executive Director, NGIS. “Google Earth Engine provides unique geospatial capability that [gives] leaders visibility and control over their environmental footprint and that of their suppliers in a way that was not previously possible.” 

Unilever commits to working towards a deforestation-free supply chain 

Unilever is a great example of a company committed to using technology in the quest to become more sustainable. Since 2020, Unilever has partnered with Google Cloud to use data for eco-friendly decision-making, particularly when it comes to sustainable commodity sourcing. 

By combining the power of cloud computing with satellite imagery and AI, we’re helping Unilever build a more holistic view of the forests, water cycles, and biodiversity that intersect its supply chain. By gaining a complete picture of these ecosystems, Unilever can detect deforestation while simultaneously prioritizing critical areas of forest and habitats in need of protection.

The cleanest cloud for your sustainable transformation

We are excited to bring Google Earth Engine and NGIS to our CPG customers to help them meet their sustainability goals. Looking ahead, we’re committed to furthering our own ambitious sustainability goals and empowering CPG brands with the technology to do more for our environment and our shared future. 

Contact your cloud seller to learn more about Tracemark sustainable sourcing solutions and how Google Cloud can help you advance your sustainability initiatives.

Explore TraceMark on the Google Cloud Marketplace

To know more about how we are helping CPGs transform digitally read this ebook

<sup>1. Google/C Space, BR, FR, DE, IN, MX, U.K., U.S., qualitative survey activity, n = 528, Nov. 24–Nov. 26, 2020.
2. Google/Ipsos, Google Sustainability, BR, FR, DE, IN, JP, U.K., U.S., n=16,959 online population 18–70, July 2021.</sup>

If you’ve been exploring recently-launched consumer generative AI tools like Bard and thinking about how to build similar experiences for your business, Generative AI App Builder, or Gen App Builder for short, is here to get you started.

Gen App Builder is part of Google Cloud’s recently announced generative AI offerings and lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. 

“Google Cloud’s leading AI technology enables STARZ customers to discover more relevant content, increasing engagement with, and the likelihood of completing the content served to them,” says Robin Chacko, EVP Direct-to-Consumer, STARZ. “We’re excited about how generative AI-powered search will help users find the most relevant content even easier and faster.”

Gen App Builder is exciting because unlike most existing generative AI offerings for developers, it offers an orchestration layer that abstracts the complexity of combining various enterprise systems with generative AI tools to create a smooth, helpful user experience. Gen App Builder provides step-by-step orchestration of search and conversational applications with pre-built workflows for common tasks like onboarding, data ingestion, and customization, making it easy for developers to set up and deploy their apps. With Gen App Builder developers can: 

• Build in minutes or hours. With access to Google’s no-code conversational and search tools powered by foundation models, organizations can get started with a few clicks and quickly build high-quality experiences that can be integrated into their applications and websites. 
• Combine the power of foundation models with information retrieval to find relevant, personalized information. Enterprises can build apps that understand user intent via natural language, and surface the right information with associated citations and attributions from a company’s public and private data. They can also fully control what data their applications access and the content or topics they want to address.
• Build multimodal apps that can respond with text, images, and other media. Gen App Builder supports not just text, but also other modalities such as images and videos. It allows developers to build apps using a combination of text and images as inputs to find information across documents, photos, and video content, enabling richer customer interactions. 
• Combine natural conversations with structured flows. Developers can granularly blend the output of foundation models with controls to ground answers in enterprise content, and step-by-step conversation orchestration to guide customers to the right answers.
• Provide the ability to transact and connect to third party apps and services. Gen App Builder makes it simple to create digital assistants and bots that not only serve content, but also connect to purchasing and provisioning systems to enable transactions from the conversational UI, and escalate customer conversations to a human agent when the context demands. 

A new generation of conversational AI experiences and assistants 

Consumers of enterprise applications expect to interact with technology in a seamless, conversational way to quickly find the information they need and act on it. Gen App Builder can help reinvent these customer and employee experiences by ingesting large, complex datasets that are specific to your company–from websites, documents, and transactional systems like billing and inventory, to emails, chat conversations, and more. These AI-powered apps can synthesize information across all of these sources to provide specific, actionable responses, using only the data you have provided. 

Some of the most popular uses are in customer service, where generative apps can contribute to increasing revenue, customer satisfaction, and customer loyalty. For example, if a retail customer reaches out to modify an order, a virtual agent can help them change it to another product. The customer doesn’t even need to provide the new product name—they can just upload an image and let the agent guide them through the rest. Watch this demo to see how a retail chatbot can use multimodal capabilities to help a consumer navigate various options on the website, including giving the customer ideas on how to use the product and even helping them complete the purchase with the ability to transact within the conversational UI. This scenario could apply to multiple industries and use cases, ranging from consumer goods and public services, to finance and internal corporate systems like intranets.

Combining the power of Google-quality search with foundation models

Finding the right information from data across the organization is a critical requirement within any enterprise. Yet it can be challenging to build high-quality enterprise search experiences with existing tools. Current systems struggle to understand user intent, are difficult to implement and customize, and don’t provide a high-quality user experience. 

One of the most exciting features of Gen App Builder is the ability to combine the power of Google-quality search with generative AI to help enterprises find the most relevant and personalized information when they need it. With Gen App Builder, enterprises can build conversational search experiences across their public and private data in minutes or hours with no coding experience. 

Enabling multimodal search across text, images and video within the enterprise is a key aspect of the search experiences in Gen App Builder. In addition to providing high-quality search results, Gen App Builder can conveniently summarize the results and provide corresponding citations in a natural, human-like fashion. Gen App Builder also automatically extracts key information from the data and enables personalized results for users. Watch this demo to see how these capabilities can come together to transform the search experience for employees at a financial services firm. The ability to integrate Google-quality search within the enterprise’s applications means they can enjoy a new level of data utilization, drive increased process efficiencies, and provide delightful experiences to their employees and customers.

“Customers have been shopping at Macy’s for generations. Being able to deliver 360° personalization and contextual recommendations will help ensure that Macy’s is still providing future generations of shoppers with a seamless, exceptional experience,” said Bennett Fox-Glassman, Senior Vice-President, Customer Journey, Macy’s. “We’ve already realized an increase in revenue per visit and conversion rates had great success using Google Cloud’s AI technology and are looking forward to exploring how these latest announcements bring together Natural Language Processing and Generative AI capabilities to deliver next-gen search and conversational experiences for our customers.”

The ability to intuitively interact with complex data across a variety of sources allows organizations to better serve their customers and deliver more relevant offerings. Combined with conversational and fulfillment abilities, the potential for improving customer engagement and employee productivity is immense. We’re excited to see how developers and enterprises use a mix of these capabilities to power new experiences and revenue opportunities.

If you’re interested in a closer look at the Gen App Builder, tune into this session at the Data Cloud & AI Summit. Take a step forward to getting hands-on and join the waitlist for our trusted tester program. And finally, bookmark our generative AI landing page to keep abreast of the latest news, updates and possibilities from this exciting new world of Gen Apps.

While you likely know that data science is the practice of making data useful, you may not have a clear landscape around the tools that can aid each stage of the data science workflow as you use machine learning to tackle your challenges.

Read on to discover the six broad areas that are critical to the process of making data useful, and some corresponding Google Cloud products and services for those areas.

https://youtube.com/watch?v=EQvLUMjz-g4%3Fenablejsapi%3D1%26

Data engineering 

Perhaps the greatest missed opportunities in data science stem from  data that exists somewhere, but hasn’t been made accessible for use in further analysis. Laying the critical foundation for downstream systems, data engineering involves the transporting, shaping, and enriching of data for the purposes of making it available and accessible.

Data ingestion and data preprocessing on Google Cloud

Here we consider data ingestion as moving data from one place to another, and data preparation the process of transformation, augmentation, or enrichment prior to consumption. Global scalability, high throughput, real-time access, and robustness are common challenges in this stage. For scalable, real-time, and batch data processing, look into building data ingestion and preprocessing pipelines with Dataflow, a managed Apache Beam service. There’s a reason why Dataflow is called the backbone of analytics on Google Cloud. 
If you’re looking for a scalable messaging system to help you ingest data, consider Cloud Pub/Sub, a global, horizontally scalable messaging infrastructure. Cloud Pub/Sub was built using the same infrastructure component that enabled Google products, including Ads, Search, and Gmail, to handle hundreds of millions of events per second. 
If you want an easy way to automate data movement to BigQuery, a serverless data warehouse on Google Cloud, look into the BigQuery Data Transfer Service. For transferring data to Cloud Storage, take a look at the Storage Transfer Service. Or, for a no-code data ingestion and transformation tool, check out Data Fusion, which has over 150 preconfigured connectors and transformations. In addition to Dataflow and Data Fusion for data preparation, Spark users may want to look at related products and features for Spark on Google Cloud.

Data storage and data cataloging on Google Cloud

For structured data, consider a data warehouse like BigQuery, or any of the Cloud Databases (relational ones like Cloud SQL and NoSQL ones like Cloud BigTable and Cloud Firestore). For unstructured data, you can always use Cloud Storage. You may also want to consider a data lake. For data discovery, cataloging, and metadata management, consider Data Catalog. For a unified solution, take a look at Dataplex, which integrates a unified data management solution with an integrated analytics experience.

Learn more about data engineering on Google Cloud

• Explore the data engineering learning path
• Discover reference patterns
• Get certified by Google Cloud as a Professional Data Engineer

Data Analysis 

From descriptive statistics to visualizations, data analysis is where the value of data starts to appear.

Data exploration, data preprocessing, and data insights

Data exploration, a highly iterative process, involves slicing and dicing data via data preprocessing before data insights can start to manifest through visualizations or simply via simple group-by, order-by operations. One hallmark of this phase is that the data scientist may not yet know which questions to ask about the data. In this somewhat ephemeral phase, a data analyst or scientist has likely uncovered some aha-moments, but hasn’t shared them yet. Once insights are shared, the flow enters the Insights Activation stage, where those insights become used to guide business decisions, influence consumer choices, or become embedded in other applications or services. 

On Google Cloud, there are many ways to explore, preprocess, and uncover insights in your data. If you are looking for a notebook-based end-to-end data science environment, check out Vertex AI Workbench, which enables you to access, analyze, and visualize your entire data estate: from structured data at the petabyte-scale in SQL with BigQuery, to processing data with Spark on Google Cloud and its serverless, auto-scaling, and GPU acceleration capabilities. As a unified data science environment, Vertex AI Workbench also makes it easy to do machine learning with TensorFlow, PyTorch, and Spark, with built-in MLOps capabilities.

Finally, if your focus is on analyzing structured data from data warehouses and insight activation for business intelligence, you may want to also consider using Looker, with its rich interactive analytics, visualizations, dashboarding tools, and Looker Blocks to help you accelerate your time-to-insight.

Learn more about data analysis on Google Cloud

• Learn about Vertex AI Workbench for a Jupyter-based fully managed notebook environment
• Learn about how you can use BigQuery for petabyte-scale data analysis
• Learn about Spark on Google Cloud
• Discover the data analyst learning path
• Explore reference patterns for common analytics use cases

Model development

From linear regression to XGBoost, from TensorFlow to PyTorch, the model development stage is where machine learning starts to provide new ways of unlocking value from your data. Experimentation is a strong theme here, with data scientists looking to accelerate iteration speed between models without worrying about infrastructure overhead or context-switching between tools for data analysis and tools for productionizing models with MLOps. 

To solve these challenges, once again, as a Jupyter-based fully managed, scalable, and enterprise-ready environment, Vertex AI Workbench makes it easy as the one-stop-shop for data science, combining analytics and machine learning, including Vertex AI services. Apache Spark, XGBoost, TensorFlow, and PyTorch are just some of the frameworks supported on Vertex AI Workbench. Vertex AI Workbench makes managing the underlying compute infrastructure needed for model training easy with the ability to scale vertically and horizontally, and with idle timeouts and auto shutdown capabilities to reduce unnecessary costs. Notebooks themselves can be used for distributed training and hyperparameter optimization, and they include Git integration for version control. Due to the significant reduction in context switching required, data scientists can build and train models 5x faster using Vertex AI Workbench than when using traditional notebooks. 

With Vertex AI, custom models can be trained and deployed using containers. You can take advantage of pre-built containers or custom containers to train and deploy your models.

For low-code model development, data analysts and data scientists can use SQL with BigQuery ML to train and deploy models (including XGBoost, deep neural networks, and PCA),  directly using BigQuery’s built-in serverless, autoscaling capabilities. Behind-the-scenes, BigQuery ML leverages Vertex AI to enable automated hyperparameter tuning, and explainable AI. For no-code model development, Vertex AI Training provides a point-and-click interface to train powerful models using AutoML, which comes in multiple flavors: AutoML Tables, AutoML Image, AutoML Text, AutoML Video, and AutoML Translation.

Learn more about model development on Google Cloud

• Learn about Vertex AI Workbench for a Jupyter-based fully managed notebook environment
• Learn more about Vertex AI

ML engineering 

Once a satisfactory model is developed, the next step is to incorporate all the activities of a well-engineered application lifecycle, including testing, deployment, and monitoring. And all of those activities should be as automated and robust as possible.

Managed datasets and Feature Store on Vertex AI provide shared repositories for datasets and engineered features, respectively, which provide a single source of truth for data and promote reuse and collaboration within and across teams. Vertex AI’s model serving capability enables deployment of models with multiple versions, automatic capacity scaling, and user-specified load balancing. Finally, Vertex AI Model Monitoring provides the ability to monitor prediction requests flowing into a deployed model and automatically alert model owners whenever the production traffic deviates beyond user-defined thresholds and previous historical prediction requests.

MLOps is the industry term for modern, well engineered ML services, with scalability, monitoring, reliability, automated CI/CD, and many other characteristics and functions that are now taken for granted in the application domain. The ML engineering features provided by Vertex AI are informed by Google’s extensive experience deploying and operating internal ML services. Our goal with Vertex AI is to provide everyone with easy access to essential MLOps services and best practices.

Learn more about ML engineering and MLOps on Google Cloud

• Follow the guides, tutorials and documentation for Vertex AI
• Watch this video to learn more about Vertex AI
• Discover the data scientist/machine learning engineer learning path
• Get certified as a Professional ML Engineer

Insights activation 

The insights activation stage is where your data has now become useful to other teams and processes. You can use Looker and Data Studio to enable use cases in which data is used to influence business decisions with charts, reports, and alerts.

Data can also influence customer decisions and as a result increase usage or decrease churn, for example. Finally, the data can also be used by other services to drive insights; these services can run outside Google Cloud, inside Google Cloud on Cloud Run or Cloud Functions, and/or using Apigee API Management as an interface.  

Learn more about insights activation on Google Cloud

• Watch this video to learn about building interactive ML apps using Looker and Vertex AI
• Learn about Looker, and Looker solutions for eCommerce, Digital Media and more
• Discover a gallery of interactive dashboards created with Data Studio 
• Watch this video to understand the difference between Cloud Run and Cloud Functions

Orchestration 

All of the capabilities discussed above provide the key building blocks to a modern data science solution, but a practical application of those capabilities requires orchestration to automatically manage the flow of data from one service to another. This is where a combination of data pipelines, ML pipelines, and MLOps comes into play.  Effective orchestration reduces the amount of time that it takes to reliably go from data ingestion to deploying your model in production, in a way that lets you monitor and understand your ML system.

For data pipeline orchestration, Cloud Composer and Cloud Scheduler are both used to kick off and maintain the pipeline. 

For ML pipeline orchestration, Vertex AI Pipelines is a managed machine learning service that enables you to increase the pace at which you experiment with and develop machine learning models and the pace at which you transition those models to production. Vertex Pipelines is serverless, which means that you don’t need to deal with managing an underlying GKE cluster or infrastructure. It scales up when you need it to, and you pay only for what you use. In short, it lets you just focus on building your data science pipelines. 

Learn more about orchestration on Google Cloud

• Read more about Cloud Composer for Airflow-based pipelines
• Try some example notebooks on Github with Vertex AI Pipelines
• Learn different ways to trigger Vertex AI Pipeline runs
• Read the whitepaper on Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning

Summary

Google Cloud offers a complete suite of data management, analytics, and machine learning tools to generate insights from data. Want to learn more? Check out the following resources:

• Building the data science driven organization from the first principles

Special thanks to the following contributors to this blogpost: Alok Pattani, Brad Miro, Saeed Aghabozorgi, Diptiman Raichaudhuri, Reza Rokni.

Editor’s note: Earth Day reminds us that we all can contribute to creating a cleaner, healthier, and more sustainable future. Google Cloud is excited to celebrate innovative startup companies developing new technology and driving sustainable change. On this year’s Earth Day we’re highlighting Enexor BioEnergy and their initiative to produce clean, sustainable energy from plastic and agro-waste with the help of Google Cloud solutions.

Picture a world without electricity where billions of people burn dangerous fuels and trash from landfills to cook meals and heat their homes. Children constantly cough, choking on toxic smoke that lingers in houses and sickens entire families. Because there are no pumps or purification plants, contaminated water is hauled in buckets from dirty rivers and polluted wells. Without power or lights, hospitals and emergency medical clinics can only provide basic services during the day.

Although it is 2022, this harsh life is a reality for billions of people living in developing countries around the world.

Producing clean energy from plastics and organic-waste

This Earth Day, let’s imagine what life would be like in these countries if people had access to safe and inexpensive bioenergy. In this world, indoor air is fresher. Clean water runs from faucets, while homes, businesses, and schools have electricity. Doctors and nurses have the power to treat patients 24 hours a day and save lives with advanced medical equipment. Local economies boom and entrepreneurs thrive.

This is the sustainable future Enexor BioEnergy and its partners are building—one village at a time. With the Enexor Bio-CHP™ system, we produce clean and sustainable energy from discarded plastics, organic, and biomass-waste such as rice and corn husks, seaweed, coconuts, and other biomatter while offsetting significant Carbon emissions. The Bio-CHP can be commissioned and easily installed in just a single day, with multiple systems installed side-by-side when more power is needed. By being offered under its novel Energy-as-a-Service business model, Enexor ensures that the Bio-CHP can generate immediate environmental positive impact and customer adoption.

Each Bio-CHP unit generates enough energy to power over 100 homes—and provides much-needed renewable electricity and thermal power for schools, businesses, manufacturing facilities, water pumps, Wi-Fi systems, and telecommunications towers. The Bio-CHP creates clean energy by safely oxidizing plastics and biomatter in a secure container using a high-temperature system to power a micro-turbine. This produces bioenergy, significantly reducing harmful greenhouse gas emissions.

By solving a community’s waste issues while providing more affordable renewable energy, the Bio-CHP also creates new economic opportunities and improved outcomes where it is installed. This includes using its inexpensive power to expand local services and offerings, and empowering local community clean up efforts by incentivizing waste collection via blockchain-enabled digital currencies and services such as PayGo. Additionally, the Bio-CHP is an ideal solution for businesses who desire to maximize their own sustainability efforts, gain greater energy resiliency, and save money on their current energy and waste costs.

Engaging in Google for Startups Accelerator: Climate Change

When developing the Bio-CHP, we realized we needed a reliable and experienced partner to help us incorporate the best in class machine learning and artificial intelligence into our technology. That’s why we joined the Google for Startups Accelerator and participated in the Google for Startups Accelerator: Climate Change.

The accelerator introduced us to other companies working to create a sustainable future and opened new opportunities for collaboration and partnerships. The accelerator also continues to give our small team access to the best of Google Cloud’s people, programs, and solutions. We especially want to highlight Google Cloud’s dedicated startup experts and the incredible technical support they provide, as well as the Google Cloud research credits we used to explore new solutions.

As an example, Enexor is developing a predictive maintenance tool that automatically adjusts Bio-CHP operations to match fuel composition. Another tool in development proactively identifies and flags potential Bio-CHP system and component failures—before they occur.

Google Cloud’s dedicated startup experts work closely with Enexor to develop predictive models for these tools and Enexor also used the Google Cloud research credits that were provided to build these advanced models on TensorFlow, Vertex AI, Cloud CDN and AutoML. Since each system is remotely monitored from Enexor’s global headquarters in Tennessee, predictive maintenance tools also increase the safety, reliability, and efficiency of Bio-CHP in off-grid locations.

Generating 13 billion kWh of clean power by 2040

In the future, Enexor plans to explore additional Google Cloud solutions such as BigQuery to crunch larger datasets, Google Data Studio to build dashboards, and perhaps even Google Cloud Tensor Processing Units (TPUs) to more efficiently process machine learning (ML) workloads.

These solutions will help Enexor to achieve three primary goals by 2040: to generate 13 billion kWh of clean power, reduce 120 million tons of CO₂, and provide one million people with access to clean and sustainable energy. With each Bio-CHP system, Enexor annually reduces up to 2,000 metric tons of CO₂ equivalent emissions by decreasing methane emissions released from landfills, offsetting fossil fuel-based power generation with carbon credits and minimizing waste disposal transportation emissions.

Enexor is now preparing to deploy the Bio-CHP in Accra, Ghana where it will convert organic and plastic manufacturing waste into sustainable energy. Enexor is also looking forward to working with the local Accra community and NGOs to collect discarded plastics and organic waste diverting it from ending in the rivers and oceans and instead using them as renewable fuel sources. We can’t wait to see what we accomplish next as we celebrate Earth Day 2022 and think about the sustainable future Enexor is helping to empower.

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.