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.”

Retail organizations constantly face financial pressure to increase sales while maintaining profit margins. Digital commerce creates new opportunities and a more competitive landscape for retailers by allowing them to reach a global customer base online, but it also exposes them to competition from larger online retailers. To be successful in this environment, retailers must not only have a strong online presence to keep up with their competition and gain market share, but also uplift the transactional customer experience to a more experiential one.

In today’s data-driven artificial intelligence-inspired business environment, organizations require complex IT infrastructure to support various functions such as prospecting, product development, marketing, and data analytics. Managing and scaling this infrastructure can be challenging, especially as needs evolve. As a result, many organizations are turning to Google Cloud as a solution for meeting their business goals, rather than simply expanding their in-house IT resources with more equipment and personnel. Specifically, retailers across the globe are betting on Kubernetes on Google Cloud to take advantage of secure, reliable, and scalable infrastructure.

Here are a few examples of worldwide retailers adapting to changing customer expectations using intelligent infrastructure solutions including Google Kubernetes Engine (GKE), the most scalable and automated fully managed Kubernetes from Google Cloud.

Haravan is a Vietnamese ecommerce platform that aims to improve the process of buying and selling products, allowing businesses to focus on creating and selling their products.

By using Google Cloud, Haravan helped small and medium-sized enterprises in Vietnam achieve double-digit growth, consistently met its 99.97% uptime commitment to clients, efficiently managed 5 times the normal amount of ecommerce activity, and facilitated the implementation of artificial intelligence-powered expansion plans.

“We have a guaranteed commitment to enable any volume of sales for clients over all channels, be it social networks, marketplaces, livestream, or website. Only Google Cloud, with the flexible autoscaling of GKE, gives us certainty to meet our guarantees even in the most massive Black Friday surges.” —Hung Le, VP of Software Engineering, Haravan

Loblaw is Canada’s food and pharmacy leader and the nation’s largest retailer. The company operates over 2,500 locations, including corporate, franchised, and associate-owned stores, and employs nearly 200,000 full- and part-time employees.

By using Google Cloud, boosted the performance of the online grocery platform, resulting in higher conversion rates and increased revenue, recovered up to 50% of Site Reliability Engineers’ time for innovation, introduced new, real-time personalization features and shopping conveniences for customers, and enhanced resiliency to protect customers and revenue.

“Moving our online grocery site to Google Cloud gave us a 4x performance increase and the capacity to handle up to three times the traffic; and we can scale up at any time.” —Hesham Fahmy, VP Technology, Loblaw

L.L.Bean is a North American retail company known for its boots and mail-order catalog, which dates back to 1912. The company has a strong online presence, with ecommerce accounting for $1 billion of its annual revenues of $1.6 billion. Like many other retailers, L.L.Bean is adopting an omnichannel sales strategy by interacting with customers through various channels including print, physical stores, its website, app, and social media.

By using Google Cloud, L.L.Bean enhanced customers’ online experience through faster page load times and access to transaction history, allowed for a focus on providing value to customers rather than managing infrastructure, and enabled the rapid release of cross-channel services by reducing development cycles.

“GKE has significantly streamlined the process of upgrading nodes and masters. By comparison, upgrading even minor releases of another container solution that L.L.Bean tested resulted in the need to rebuild that solution’s clusters four times.” —Randy Dyer, Enterprise Architect, L.L.Bean

LPP is a Polish fashion retailer established in 1991 by Lubianiec and Piechocki, whose initials make up the company’s name. LPP currently manages five clothing brands that are popular in 38 countries across Europe, Africa, and Asia, and has over 24,000 employees based in its main offices in Central and Eastern Europe. Growing demand for its ecommerce services led LPP to migrate from an on-premises setup to Google Cloud, harnessing automation to ensure great shopping experiences globally.

By using Google Cloud, LPP promoted a DevOps culture among developers through streamlined deployment of new features, provided 90% more time for engineers to work on innovative solutions instead of managing infrastructure, instantly created and updated new VMs, allowing developers to quickly launch new features, ensured a seamless online shopping experience by automatically adjusting capacity to meet demand.

“GKE enables us to deploy new features for our ecommerce sites very quickly. Previously, it took weeks to launch new instances for each brand. Today, it takes seconds: we simply launch a new machine, deploy the code, and changes are reflected automatically across our environment.” —Marek Maciejewski, Head of IT Service Operations, LPP

Noon.com, based in Riyadh, Saudi Arabia, is a local ecommerce marketplace focused on serving the Middle East. The company aims to become the top online retailer in the region, supporting the growth of a digital economy for both consumers and local businesses.

By using Google Cloud, Noon.com built its ecommerce platform to access self-managed services, allowed developers to establish a fully operational staging environment within two weeks, provided uninterrupted service to nearly four times as many daily users during busy seasons using autoscaling on GKE, used real-time data streaming on BigQuery to inform business decisions and personalize the customer experience, and achieved 99.999% availability with no downtime for planned maintenance or schema changes using a fully managed relational database.

“Google Cloud-managed services are playing a major role in enabling Noon.com customers to get their shopping done whenever they need it, without experiencing any delays or glitches, and without us having to lose sleep at night to ensure our platform is functioning as it should.” —Alex Nadalin, SVP of Engineering, Noon.com

In conclusion, the retail industry is constantly evolving and retailers must stay up-to-date with the latest technology and customer preferences to remain competitive. Digital commerce has changed the landscape of retail, allowing businesses to reach a global customer base but also increasing competition. The use of Kubernetes on Google Cloud can help retailers improve the customer experience, streamline internal processes, and make data-driven and AI-inspired decisions. By embracing these changes, retailers can stay ahead in a constantly evolving industry. Get started today with an exclusive workshop, Unlocking efficiency and innovation with Kubernetes on Google Cloud.

Editor’s note: Today we hear from ForgeRock, a multinational identity and access management software company with more than 1,100 enterprise customers, including a major public broadcaster. In total, customers use the ForgeRock Identity Platform to authenticate and log in over 45 million users daily, helping them manage identity, governance, and access management across all platforms, including on-premises and multicloud environments. 

Operating at that kind of scale isn’t easy. In this blog post, ForgeRock Engineering Director, Warren Strange discusses the three things that help make their developers efficient and productive, and the Google Cloud tools they use along the way. 

At ForgeRock, we’ve been an early adopter of Kubernetes, viewing it as a strategic platform. Running on Kubernetes allows us to drive multicloud support across Google Kubernetes Engine (GKE), Amazon (EKS), and Azure (AKS). So no matter which cloud our customers are running on, we are able to seamlessly integrate our products into customers’ environments. 

Making it easier for ForgeRock’s developers and operators to build, deploy and manage applications has been crucial in our ability to continually provide high quality solutions for our customers. We’re always looking for tools to improve productivity and keep our developers focused on coding instead of configuration. Google Cloud’s suite of DevOps tools have streamlined three specific practices to help keep our developers productive: 

1. Make developers productive within IDEs

Developer productivity is core to the success of any organization, including ForgeRock. Since developers spend most of their time within their IDE of choice, our goal at ForgeRock has been to make it easier for our developers to write Kubernetes applications within the IDEs they know and love. Cloud Code helps us precisely with that: it makes the process of building, deploying, scaling, and managing Kubernetes infrastructure and applications a breeze. 

In particular, working with the Kubernetes YAML syntax and schema takes time, and a lot of trial and error to master. Thanks to YAML authoring support within Cloud Code, we can easily avoid the complicated and time consuming task of writing YAML files at ForgeRock. With YAML authoring support, developers save time on every bug. Cloud Code’s inline  snippets, completions, and schema validation, a.k.a. “linting,” further streamline working with YAML files. 

The benefits of Cloud Code extend to local development as well. Iterating locally on Kubernetes applications often requires multiple manual steps, including building container images, updating Kubernetes manifests, and redeploying applications. Doing these steps over and over again can be a chore. Cloud Code supports Skaffold under the hood, which tracks changes as they come and automatically rebuilds and redeploys—reducing repetitive development tasks. 

Finally, developing for Kubernetes usually involves jumping between the IDE, documentation, samples etc. Cloud Code reduces this context switching with Kubernetes code samples. With samples, we can get new developers up and running quickly. They spend less time learning about configuration and management of the application—and spend more time on writing and evolving the code.

2. Drive end-to-end automation

To further improve developer productivity, we’ve focused on end-to-end automation: from writing code within IDEs, to automatically triggering CI/CD pipelines and running the code in production. In particular, Tekton, Cloud Build, Container Registry, and GKE have been critical to Forgerock as we streamline the flow of code, feedback and remediation through the build and deployment processes. The process looks something like this:

We begin by developing Kubernetes manifests and dockerfiles using Cloud Code. Then we use Skaffold to build containers locally, while Cloud Build helps with continuous integration (CI). The Cloud Build GitHub app allows us to automate builds and tests as part of our GitHub workflow. Cloud Build is differentiated from other continuous integration tools since it is fully serverless. It scales up and scales down in response to load, with no need for us to pre-provision servers or pay in advance for additional capacity. We pay for the exact resources we use. 

Once the image is built by Cloud Build, it is stored, managed, and secured in Google’s Container Registry. Just like Cloud Build, Container Registry is serverless, so we only pay for what we  use. Additionally, since Container Registry comes with automatic vulnerability scanning, every time we upload a new image to Container Registry, we can also scan it for vulnerabilities. 

Next, a Tekton pipeline is triggered, which deploys the docker images stored in Container Registry and Kubernetes manifests to a running GKE cluster. Along with Cloud Build, Tekton is a critical part of our CI/CD process at ForgeRock. Most importantly, since Tekton comes with standardized Kubernetes-native primitives, we can create continuous delivery workflows very quickly.

After deployment, Tekton triggers a functional test suite to ensure that the applications we deploy perform as expected. The test results are posted to our team Slack channel so all developers have instant access and insights about each cluster. From there, we are able to provide our customers with their finished product request.

3.  Leverage multicloud patterns and practices

The industry has seen a shift towards multicloud. Organizations have adopted multicloud strategies to minimize vendor lock-in, take advantage of best-in-class solutions, improve cost-efficiencies, and increase flexibility through choice. 

At ForgeRock, we’re big proponents of multicloud. Part of that comes from the fact that our identity and access management product works across Google Cloud, AWS, and Azure. Developing products using open-source technologies such as Kubernetes has been particularly helpful in driving this interoperability. 

Tekton has been another critical project that has allowed us to prevent vendor lock-in. Thanks to Tekton, our continuous delivery pipelines can deploy across any Kubernetes cluster. Most importantly, since Tekton pipelines run on Kubernetes, these pipelines can be decoupled from the runtime. Like Tekton and Kubernetes, both Cloud Build and Container Registry are based on open technologies. Community-contributed builders and official builder images allow us to connect to a variety of tools as a part of the build process. And finally, with support for open technologies like Google Cloud buildpacks within Cloud Build, we can build containers without even knowing Docker. 

Making it easier for developers and operators to build, deploy and manage applications is critical for the success of any organization. Driving developer productivity within IDEs, leveraging end-to-end automation, and support for multi-cloud patterns and practices are just some of the ways we are trying to achieve this at ForgeRock. To learn more about ForgeRock, and to deploy the ForgeRock Identity Platform into your Kubernetes cluster, check out our open-source ForgeOps repository on GitHub.

Cloud transformation and the adoption of modern DevOps technology presents both opportunities and challenges for IT compliance functions. With DevOps style application development, the feedback loop for developers and engineers is much tighter than with traditional application development pipelines, enabling speed and agility of application release cycles. While speedy CI/CD is a critical advantage of DevOps, it also shifts compliance left in the development timeline, and therefore puts pressure on the IT risk & compliance organization to modernize their approach to regulatory compliance as well. With the ongoing shift towards cloud technologies and DevOps, modernization of regulatory compliance is no longer optional for an IT compliance function

Compliance modernization is a broad mandate that spans the way the function is governed; the tools, technology, and analytics it uses; the number and nature of its connections to other parts of the business; verifiability and auditability of the controls’ evidence, the expectations assigned to it; and more.

Public cloud technology is becoming a core part of many industries today, and with this comes some potential risks such as cloud misconfigurations exposing intellectual property, loss of physical control of assets, skillset scarcity around cloud based security and compliance. 

Given the constantly changing risk landscape, it is critical that regulations more closely align to address these risks. As regulations and risks evolve, the aim of a modern compliance function is to help an organization stay compliant as it goes through a digital transformation. As organizations go through digital transformation, IT compliance also needs to transform — via upgrading the technology stack, modifying the business processes and most importantly re-skilling people to become cloud aware.

Today we are releasing the new paper by Google Cloud’s Office of the CISO. In the paper we reveal a new approach for modernizing your compliance approach using modern approaches and Google Cloud toolsets. Your team can leverage the paper to add value to enterprises, both by charting a course to the safe use of cloud technology and by reducing risk through the use of the public cloud.

Read the paper “Assuring Compliance in the Cloud.”

Also, review these related resources:

• “Risk Governance of Digital Transformation in the Cloud“ paper
• “Making Compliance Cloud-native” (episode 14) with Zeal Somani
• Google Cloud Compliance resource center
• Our compliance blueprints: PCI DSS on GKE and GCP FedRAMP Blueprint
• Google Cloud security best practices center