AgroStar: Small farms in India getting big help from the cloud
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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.”
ESG Did the Math: It’s Cheaper and Smarter to Migrate Enterprise Data Warehouses to Google BigQuery. Way Smarter
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Enterprise data warehouses (EDWs) are often deemed the most valuable asset in the data center, serving as the backbone of the business. The ongoing insight gained from these solutions has justified the significant up-front capital investments and ongoing operational costs, but the rigidity of the traditional EDW is forcing organizations to reevaluate their approach to analytics and business intelligence.
While legacy EDW solutions were all about throwing as much computational power as possible at a relatively static data set, with the inflow of new and valuable sources of data and the emergence of all-encompassing analytics initiatives, the success of today’s EDW solutions depends more on operational and resource agility than raw horsepower.
Being able to dynamically adjust to the needs of the business, integrate into operational processes, and quickly react to emerging opportunities can place an organization at a distinct competitive advantage.
Today’s EDW solutions must act as a global repository of information, provide the agility to scale up or down on demand, and seamlessly integrate with other analytics tools and services used throughout a data-driven organization.
Over the last two years, ESG has conducted detailed studies quantifying the economic value of Google data analytics services. The first evaluated Google BigQuery compared to on-premises Hadoop and AWS redshift. The second focused on Google DataProc compared to DIY Spark and Hadoop approaches. Here’s the next iteration of our economic analysis, extending the BigQuery study to incorporate a comparison to legacy enterprise data warehouses, both on-premises and in the cloud.
Through publicly available pricing and in-depth qualitative customer interviews, ESG was able to assert a base set of assumptions that power a dynamic model, incorporating up-front capital investments, deployment and migration costs, expected monthly cloud costs, administrative costs, and operational costs associated with legacy on-premises EDWs, cloud-based EDWs, and Google BigQuery.
The crux of the results show organizations can save up to 52% by using BigQuery over on-premises EDWs and up to 41% over cloud-based EDWs. Unlike legacy on-premises EDWs, BigQuery provides organizations with the key abilities that are essential to delivering a modern EDW solution, most notably the ability to integrate across other Google Cloud Platform services, including its market leading AI-based solutions and services.
Although not called t out directly in the published report, ESG’s models indicate that the savings achieved by migrating an on-premises EDW solution to Google BigQuery may actually be more cost effective than simply continuing to operate an existing on-premises EDW solution.
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Sure, machine learning is becoming a business imperative, but how does it work in practice—and what are the benefits for IT managers?
That’s the subject of a new step-by-step guide to solving business and IT problems with artificial intelligence and ML, based on insights gathered by IDG Research Services.
Its publication comes at a time when technology departments face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.
It has real-life examples such as the health services company that used ML to reduce support ticket-resolution time from 48 minutes to six.
In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.
The guide also includes concrete tips for new ML adopters. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.
Download the guide now!
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How to Build and Maintain Spark and Hadoop Clusters Quickly, Easily and Inexpensively
How often have you struggled to create Spark and Hadoop clusters only to have them become unstable?
Building and maintaining clusters that are the building blocks of big data analytics should not be a time-consuming, repetitive, and expensive process. But with the current set of tools, it often is.
Google Cloud Dataproc solves this. It is a fast, easy-to-use, fully-managed cloud service for running Spark and Hadoop clusters in a simple, more cost-efficient way.
Operations that used to take hours or days take now seconds or minutes instead, and you pay only for the resources you use (with per-second billing).
Watch this five-minute demo to find out how—with just a few clicks—you can set up clusters that build and heal themselves. You’ll find out how to creating a large Dataproc cluster with preemptible VMs, which help run large clusters at a low total cost.
Post-COVID: Times Driven by Data Analytics and Intelligence
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As we think about economic recovery from COVID-19—both inside Google and outside through working with Google Cloud customers—we’ve made many important observations. Among them is the recognition that the ways software developers and IT practitioners work together will shift in the post COVID-19 world. Our economic recovery today will look different than past recoveries, and on a fundamental level, the way we innovate will be different than it’s ever been before.
Right now, we’re entering a new phase of cloud computing, where businesses have shifted from making tactical infrastructure decisions, to making larger IT decisions with an eye towards enabling transformation throughout the company. Data, and what we can do with that data, is key to this transformation. And how companies put data in the hands of every employee to help catalyze transformation and solve the most important and impactful opportunities in their industries is at the core.
A recent Google-commissioned study by IDG highlighted the role of data analytics and intelligent solutions when it comes to helping businesses separate from their competition. The survey of 2,000 IT leaders across the globe reinforced the notion that the ability to derive insights from data will go a long way towards determining which companies win in this new era.
Data analytics and intelligence were prioritized during COVID-19
The results of the IDG study show a separation amongst those organizations that embrace the capabilities of today’s data analytics and AI/ML tools and those that do not. When COVID-19 hit, many organizations cancelled IT initiatives, with 55% of respondents delaying or cancelling at least one technology project. However, 32% of respondents accelerated or introduced initiatives around building out or improving the use of data analytics and intelligence. IT leaders realize how critical data is to their future success, even when resources are scarce.
Digital-focused companies are faster to embrace advanced intelligence tools
Furthermore, enthusiasm for big data analytics, AI, and ML technologies is highest among companies who are further along in their digital transformation journeys. Fifty-four percent of companies who identify as “Fully digitally transformed” or “Digital native” are using or considering using these tools, vs. the global average of 37%. And, these same organizations are embracing the promise of AI more than their peers. Forty-eight percent felt that “Embedded AI across our full stack of cloud solutions will be critical” vs 39% of digital conservatives. These companies realize these digital tools enable them to be more resilient, agile, and prepared for whatever the future brings.
Companies are turning to cloud to maximize insights from data
As companies tap into the promise of data analytics and AI/ML, they are turning to cloud for help. When considering which cloud providers to work with, 78% of respondents said big data analysis is a “must have” or a “major consideration,” which placed this capability at the top of the list of consideration factors. This is not surprising, as cloud solutions address the most common pain points and barriers to innovation. Three of the respondents’ top four areas impeding innovation are addressed by cloud: Insufficient IT & developer skill sets (1st), security risks and concerns (2nd), and legacy systems and technologies (4th). Plus, cloud makes it easier to quickly launch a project, scale up or scale down, and pay for only what you use.
COVID-19 changed the very nature of business, and of IT. It forced IT leaders to decide where to put their scarce resources and big data analytics and AI/ML were, understandably, at the top of the list. To learn more about the findings, download the IDG report “No turning back: How the pandemic reshaped digital business agendas.”
This Diagnostic Company is Revolutionising Healthcare Delivery with AI
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Dr. Elliot Smith cannot be accused of lacking ambition. A high achiever with a Ph.D. in Electrical Engineering and a specialist in magnetic resonance imaging (MRI) systems, Smith aims to deliver top quality healthcare to anyone in the world — regardless of their location or wealth.
Dr. Smith has already made strides on this journey with his Brisbane, Queensland-headquartered business, Maxwell MRI. “I saw there was a big gap in the market around automating the diagnosis of health conditions,” he says. “Existing processes were typically manual and involved a lot of people.”
Artificial intelligence (AI) and machine learning can remove a key obstacle to scaling out medicine and improve the efficiency and accuracy of diagnosing conditions, the healthcare entrepreneur believes.
“Our grand vision is to build an AI doctor that anyone can receive affordable support from and connect to in order to obtain results,” explains Dr. Smith.
Maxwell MRI presently enables clinicians to submit anonymised MRI scans to a machine learning enabled AI platform to help diagnose prostate cancer. The service is sold to clinicians who can then charge a per-session fee to clients. As well as obtaining results for individual cases, the MRI scans and associated information is used to ‘train’ the platform to deliver accurate diagnoses faster and in a more affordable way than existing systems do.
Dr. Smith and his team started by running a number of functions and processes on a single server with graphics processing units (GPUs) and sizable hard disk capacity. However, this infrastructure could not scale to support the planned growth of the business. Each case Maxwell MRI processes involves about 200MB of data in MRI scans alone. Once supplementary data, blood test result, pathology results and genetic information is included, this load can reach more than 1GB of data per patient.
The business aimed to process 150,000 cases by the end of 2018. This required a service that could deliver massive scale in data storage and compute, and could easily be accessed from any location. “We wanted to move from three GPUs to 30 GPUs without having to buy more servers or other associated equipment, so the cloud was the natural next step,” says Dr. Smith.
Maxwell MRI evaluated Google Cloud Platform (GCP) and determined that the managed services component of GCP would remove the burden of infrastructure deployment and administration. In addition, Google Cloud Machine Learning Engine would enable the business to scale to as many GPUs as needed to meet demand.
Maxwell MRI started with some small experiments to determine that GCP met all its requirements and completed its migration to the platform in February 2017. “We really started to scale up the data we had and consequently our computing requirements at that time,” Dr. Smith says.
The Maxwell MRI platform features an upload service that enables clinicians to upload imaging and associated data. This service triggers several different upload pipelines that clean and standardise data. They then write imaging data to Google Cloud Storage, and more structured data to a combination of Google Cloud Datastore and Google Cloud Spanner.
The platform then converts the information into records that can be used to ‘train’ new machine learning configurations or run evaluations through existing machine learning pipelines.
“The tasks we perform including segmenting various anatomical regions for analysis and sending those results back into Google Cloud Storage,” says Dr. Smith. “This then commences that repeated process of running Google Cloud Dataflow pipelines and machine learning algorithms, and presenting those outcomes back to the clinicians.”
Existing Literature Validated
The data processed and analysed to date has, Dr Smith says, enabled Maxwell MRI to help validate existing literature that indicates clinicians lack confidence in existing early-stage testing procedures for prostate cancer. This prompts them to move quickly to the biopsy stage to assure themselves their diagnosis is valid. “New technologies have a lot of potential to rectify this situation and guide treatment to be more accurate, specific and cost-effective,” he says.
Results Delivered in 10-15 Minutes
More specifically, using GCP has enabled Maxwell MRI to guarantee to clinicians that results will be delivered within minutes. “Clinicians are used to getting results back in two days to a week,” says Dr. Smith. “We’re saying that with our platform running on GCP we’ll deliver you results in 10 to 15 minutes, regardless of the number of patients coming in.”
Running on GCP has enabled the business to accelerate its development cycles, test new ideas easily on a subset of data, test in parallel and deliver new services considerably faster than in another environment. In addition, the flexible GCP charging model aligned with the ability to scale compute capabilities quickly and easily has enabled the fledgling business to control its costs.
Google technologies are poised to play an integral role in the business’s future. “With Google available, it doesn’t make sense for us to use our own infrastructure,” Dr. Smith says. “Our expertise in AI, machine learning and clinical engagement complements cloud platform specialties of infrastructure, managed services and ease of use. We see a bright future ahead in helping to transform healthcare globally.”
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