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.”

Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases.

Some of the momentum is more foundational, such as the hundreds of academic citations that Google AI researchers earn each year, or products like Google Cloud Vertex AI accelerating ML development and experimentation by 5x, with 80% fewer lines of code required. Some are more concrete, like mortgage servicer Mr. Cooper using Google Cloud Document AI to process documents 75% faster with 40% cost savings; Ford leveraging Google Cloud AI services for predictive maintenance and other manufacturing modernizations; and customers across a wide range of industries deploying ML platforms atop Google Cloud.

Together, these proof points reflect our belief that AI is for everyone, and that it should be easy to harness in workflows of all kinds and for people of all levels of technical expertise. We see our customers’ accomplishments as validation of this philosophy and a sign that we are taking away the right things from our conversations with business leaders. Likewise, we see validation in recognition from analysts, which recently includes Google being named a Leader by

Gartner® in the 2022 Magic Quadrant™ for Cloud AI Developer Services report

Forrester in the Forrester Wave™: AI Infrastructure, Q4 2021 report, the Forrester Wave™: Document-Oriented Text Analytics Platforms, Q2 2022 report, and The Forrester Wave™: People-Oriented Text Analytics Platforms, Q2 2022 report

In June, we talked about four pillars that guide our approach to creating products for MLOps and to accelerate development of ML models and their deployment into product. In this article, we’ll look more broadly at our AI and ML philosophy, and what it means to create “AI for everyone.”

AI should be for everyone

One of the pillars we discussed in June was “meeting users where they are,” and this idea extends far beyond products for data scientists. Technical expertise should not be a barrier to implementing AI—otherwise, use cases where AI can help will languish without modernization, and enterprises without well-developed AI practices will risk falling behind their competitors.

To this end, we focus on creating AI and ML services for all kinds of users, e.g.:

• DocumentAI, Contact Center AI, and other solutions that inject AI and ML into business workflows without imposing heavy technical requirements or retraining on users;
• Pre-trained APIs, ranging from Speech to Fleet Optimization, that let developers leverage pre-trained ML models and free them from having to develop core AI technologies from scratch;
• BigQuery ML to unite data analysis tasks with ML;
• AutoML for abstracted and low-code ML production without requiring ML expertise;
• Vertex AI to speed up ML experimentation and deployment, with every tool you need to build deploy and the lifecycle of ML projects
• AI Infrastructure options for training deep learning and machine learning models cost effectively. Including Deep Learning VMs optimized for data science and machine learning tasks and AI accelerators for every use case, from low-cost inference to high-performance training.

It’s important to provide not only leading tools for advanced AI practitioners, but also leading AI services for users of all kinds. Some of this involves abstracting or automating parts of the ML workflow to meet the needs of the job and technical aptitude of the user. Some of it involves integrating our AI and ML services with our broader range of enterprise products, whether that means smarter language models invisibly integrated into Google Docs or BigQuery making ML easily accessible to data analysts. Regardless of any particular angle, AI is turning into a multi-faceted, pervasive technology for businesses and users the world over, so we feel technology providers should reflect this by building platforms that help users harness the power of AI by meeting them wherever they are.

How we’re powering the next generation of AI

Creating products that help bring AI to everyone requires large research investments, including in areas where the path to productization may not be clear for years. We feel a foundation in research combines with our focus on business needs and users to inform sustainable AI products that are in keeping with our AI principles and encourages responsible use of AI.

Many of our recent updates to our AI and ML platforms began as Google research projects. Just consider how DeepMind’s breakthrough AlphaFold project has led to the ability to run protein prediction models in Vertex AI. Or how research into neural networks helped create Vertex AI NAS, which lets data science teams train models more accurately with lower latency and power requirements.

Research is crucial, but also only one way of validating an AI strategy. Products have to speak for themselves when they reach customers, and customers need to see their feedback reflected as products are iterated and updated. This reinforces the importance of seeing customer adoption and success across a range of industries, use cases, and user types. In this regard, we feel very fortunate to work with so many great customers, and very proud of the work we help them accomplish.

I’ve already mentioned Ford and Mr. Cooper, but those are just a small sampling. For example, Vodafone Commercial’s “AI Booster” platform uses the latest Google technology to enable cutting-edge AI use cases such as optimizing customer experiences, customer loyalty, and product recommendations. Our conversational AI technologies are used by companies ranging from Embodied, whose Moxie robot helps children overcome developmental challenges, to HubSpot connecting meeting notes to CRM data. Across our products and across industries around the world, customer stories grow by the day.

We also see validation in our partner network. As we noted in the pillars discussed in June, partners like Nvidia help us to ensure customers have freedom of choice when building their AI stacks, and partners like Neo4j help our customers to expand our services into areas like graph structures. Partners support our mission to bring AI to everyone, helping more customers use our services for new and expanded use cases.

Accelerating the momentum

Overall, to create products that reflect AI’s potential and likely future ubiquity, we have to take all of the preceding factors, from research to customer and analyst conversations to working with partners, and turn them into products and product updates. We’ve been very active over the last year, from the launch of Call Center AI Platform in March, to the new Speech model we released in May, to a range of announcements at the Google Cloud Applied ML Summit in June. We have much more planned in coming months, and we’re excited to work with customers not just to maintain the pace of AI momentum, but to accelerate it. To learn more about Google Cloud’s AI and ML services, visit this link or browse recent AI and ML articles on the Google Cloud Blog.

GARTNER and MAGIC QUADRANT are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job. Also, end to end use cases require Spark to be used along with technologies like TensorFlow, and programming languages like SQL and Python. Today, these operate in silos, with Spark on unstructured data lakes, SQL on data warehouses, and TensorFlow in completely separate machine learning platforms. This increases costs, reduces agility, and makes governance extremely hard; prohibiting enterprises from making insights available to the right users at the right time.

Announcing Spark on Google Cloud, now serverless and integrated

We are excited to announce Spark on Google Cloud, bringing industry’s first autoscaling serverless Spark, seamlessly integrated with the best of Google Cloud and open source tools, so you can effortlessly power ETL, data science, and data analytics use cases at scale. Google Cloud has been running large scale business critical Spark workloads for enterprise customers for 6+ years, using open source Spark in Dataproc. Today, we are furthering our commitment by enabling customers to:

1. Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
2. Enable data users of all levels: Connect, analyze, and execute Spark jobs from the interface of users’ choice including BigQuery, Vertex AI or Dataplex, in 2 clicks, without any custom integrations.
3. Retain flexibility of consumption: No one size fits all. Use Spark as serverless, deploy on Google Kubernetes Engine (GKE), or on compute clusters based on the requirements.

With Spark on Google Cloud, we are providing a way for customers to use Spark in a cloud native manner (serverless), and seamlessly with tools used by data engineers, data analysts, and data scientists for their use cases. These tools will help customers on their way to realize the data platform redesign they have embarked on.

“Deutsche Bank is using Spark for a variety of different use cases. Migrating to GCP and adopting Serverless Spark for Dataproc allows us to optimize our resource utilization and reduce manual effort so our engineering teams can focus on delivering data products for our business instead of managing infrastructure. At the same time we can retain the existing code base and knowhow of our engineers, thus boosting adoption and making the migration a seamless experience.”—Balaji Maragalla, Director Big Data Platform, Deutsche Bank

“We see serverless Spark playing a central role in our data strategy. Serverless Spark will provide an efficient, seamless solution for teams that aren’t familiar with big data technology or don’t need to bother with idiosyncrasies of Spark to solve their own processing needs. We’re excited about the serverless aspect of the offering, as well as the seamless integration with BigQuery, Vertex AI, Dataplex and other data services.” —Saral Jain, Director of Engineering, Infrastructure and Data, Snap Inc.

Dataproc Serverless for Spark

Per IDC, developers spend 40% time writing code, and 60% of the time tuning infrastructure and managing clusters. Furthermore, not all Spark developers are infrastructure experts, resulting in higher costs and productivity impact. With serverless Spark, developers can spend all their time on the code and logic. They do not need to manage clusters or tune infrastructure. They submit Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job. Furthermore, while Spark users today pay for the time the infrastructure is running, with serverless Spark they only pay for the job duration.

Spark through BigQuery

BigQuery, the leading data warehouse, now provides a unified interface for data analysts to write SQL or PySpark. The code is executed using serverless Spark seamlessly, without the need for infrastructure provisioning. BigQuery has been the pioneer for serverless data warehousing, and now supports serverless Spark for Spark-based analytics.

Spark through Vertex AI

Data scientists no longer need to go through custom integrations to use Spark with their notebooks. Through Vertex AI Workbench, they can connect to Spark with a single click, and do interactive development. With Vertex AI, Spark can easily be used together with other ML frameworks like TensorFlow, Pytorch, Sci-kit learn, and BigQuery ML. All the Google Cloud security, compliance, and IAM are automatically applied across Vertex AI and Spark. Once you are ready to deploy the ML models, the notebook can be executed as a Spark job in Dataproc, and scheduled as part of Vertex AI Pipelines.

Spark through Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale. Now, you can use Spark on distributed data natively through Dataplex. Dataplex provides a collaborative analytics interface, with 1-click access to SparkSQL, Notebooks, or PySpark, and the ability to save, share, search notebooks and scripts alongside data.

Flexibility of consumption

We understand one size does not fit all. Spark is available for consumption in 3 different ways based on your specific needs. For customers standardizing on Kubernetes for infrastructure management, run Spark on Google Kubernetes Engine (GKE) to improve resource utilization and simplify infrastructure management. For customers looking for Hadoop style infrastructure management, run Spark on Google Compute Engine (GCE). For customers, who’re looking for no-ops Spark deployment, use serverless Spark! 

ESG Senior Analyst Mike Leone commented, “Google Cloud is making Spark easier to use and more accessible to a wide range of users through a single, integrated platform. The ability to run Spark in a serverless manner, and through BigQuery and Vertex AI will create significant productivity improvement for customers. Further, Google’s focus on security and governance makes this Spark portfolio useful to all enterprises as they continue migrating to the Cloud.”

Getting started

Dataproc Serverless for Spark will be Generally Available within a few weeks. BigQuery and Dataplex integration is in Private Preview. Vertex AI workbench is available in Public Preview, you can get started here. For all capabilities, you can request for Preview access through this form.

You can work with Google Cloud partners to get started as well.

“We are excited to partner with Google Cloud as we look to provide our joint customers with the latest innovations on Spark. We see Spark being used for a variety of analytics and ML use cases. Google is taking Spark a step further by making it serverless, and available through BigQuery, Vertex AI and Dataplex for a wide spectrum of users.” —Sharad Kumar, Cloud First data and AI Lead at Accenture

For more information, visit our website or the watch announcement video and our conversation with Snap at Next 2021.

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

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.

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