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A Quick Guide to Cloud Monitoring
Cloud Monitoring is a tool that allows you to gain visibility into the performance, availability, and health of your applications and infrastructure. In this video, we show you what Cloud Monitoring is and how you can use it to custom define service-level objectives (SLOs), monitor application metrics, and the overall health of your applications infrastructure. Watch to learn how you can use Cloud Monitoring!
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.”
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Every business is a digital business. That’s what you’ll hear from technology folks these days. But, what exactly is a digital business? How does one define it?
Simply put, digital businesses are those that have thoroughly capitalized on the opportunity to connect people with technology. There are four parts to a digital business:
Real-time data and analytics: To stay relevant in the age of Big Data, businesses must analyze copious amounts of data to derive actionable insights—both from historical data, and in real time.
Fast, flexible application development: Rapid and continuous delivery of software to your stakeholders is no longer optional. Businesses need platforms for their applications strategy — from using container-based development tools to fully managed serverless platforms.
Secure, reliable infrastructure: How secure is your on-prem datacenter? What happens if it goes down? How many dedicated security engineers do you have on staff? What’s the cost of a system upgrade? What digital businesses need is a secure and reliable infrastructure that can power their applications.
Constant collaboration and productivity: Digital businesses are designed to keep teams seamlessly connected not only to each other, but also to the applications that keep the company running. This enables everything and everyone to work together, no matter where they sit—across the office or across the ocean.
Download this infographic to know more.
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HSBC Leverages Google Cloud to Deliver Exceptional Cx for 37 Million Customers
HSBC is the world’s largest international bank that has been in existence for 150 years. HSBC is present in 70 countries, has 37 million customers and is the number one bank in trade finance or cross-border finance. HSBC is a systemically important financial institution that is heavily regulated.
Rapid growth in data and aggressive adoption of digital channels by customers drove HSBC to make a significant transformation in their existing IT infrastructure.
The existing IT infrastructure at HSBC has been around for 30 to 40 years and with the growing need to analyze, store and process massive amounts of data it gradually became redundant. To overcome these challenges and balloon the organizations’ compute capacity HSBC decided to adopt a cloud-first strategy.
“Our existing systems are robust, scalable and can do a great job, but they do not have the database structure that allows us to run analytics and machine learning,” says Darryl West, Group CIO, HSBC.
“We are a bank at the core of our business, but we also have a significant technology company embedded within the organization. I asked the management team do we really want to compete with cloud providers, like Google, are we really going to try and do what they do, as well as they do it? Our conclusion was it was better for our business if we do a cloud-first strategy,” says West.
HSBC has been working with Google on some of the most critical business problems. The initial uses cases are typically characterized by business problems that have very large data sets and require very intense computing capability in short bursts. These include anti-money laundering, finance liquidity reporting, risk analytics, risk reporting, valuation services and more.
Watch the full video to understand how HSBC is leveraging Google Cloud Platform to enhance customer experience and deliver continuous innovations.
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Any organization that has made a significant investments in Paas or IaaS capabilities requires a FinOps (financial operations) strategy. It involves linking their cloud migration business cases with value metrics, creating detailed cost visibility dashboards and having an automated expense control to ensure value realization from the cloud transformation journey. To fast-track your FinOps journey on Google Cloud, experts offer a detailed intro on the FinOps concept, the five Cloud FinOps pillars and important metrics for business value realization in this whitepaper.
To keep up with the digital transformations and demands of an ever-evolving virtual workforce, Google Cloud has distilled insights from several organizations that began their cloud migration journey and the FinOps Foundation community. Download the ‘Maximize Business Value with Cloud FinOps’ document to build a solid foundation for your organization’s cloud FinOps.
TVG Network Turns to Google Cloud and Saves $0.5 Million a Year
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Google Cloud Results
- Maximizes revenue by allowing customers to place bets faster and more confidently
- Scales for major racing events with 80% less IT involvement and up to $500,000 annual savings
- Improves time to market for new product releases by more than 30x
- Helps enable richer mobile experiences to keep fans engaged
- Processes up to 6,000 bets per minute
The first Saturday of May is the biggest horse racing event in North America each year. Minutes before the race, millions of dollars in online bets will flow in through advanced deposit wagering (ADW) operators such as TVG Network. For TVG’s IT team, it’s a high-stakes game: If wagering systems can’t handle thousands of requests per second, revenue and customers will be lost.
To avoid downtime before a major race, TVG used to bombard its systems with ad-hoc load tests a month in advance. Before each big race event, a team of seven people spent eight hours a week deploying new infrastructure and testing various scenarios. But with complex legacy systems and manual processes, the team’s efforts could only go so far. If an unexpected system issue or undetected bottleneck was found during the run-up to the big race, all bets were off.
After a brush with downtime in 2016, TVG decided to move its ADW application to the cloud, taking the opportunity to rewrite the application to take advantage of modern, container-based architectures. After a short period of development on a different cloud services provider, TVG moved to Google Cloud Platform using Google Kubernetes Engine to automate container management and orchestration.
“We chose Google Cloud Platform because it was the most reliable, cost-effective, and automated cloud solution available,” says Tim Morrow, CTO at TVG Network. “We get better security, strong compliance, and the peace of mind that when the biggest race day rolls around, we won’t have any downtime.”
Placing the right bet
Moving to Google Cloud Platform gives TVG a variety of options in different regions and availability zones to satisfy regulatory requirements. Google Cloud Platform offers continuous availability and transparent maintenance, with no scheduled downtime or patching requirements.
“For our online wagering site, we prefer Google’s philosophy of continuous availability and live migration,” says Tim. “Having to plan for scheduled downtime of cloud instances just seems ridiculous in this day and age. And with Google Cloud Platform, we get much more consistent performance as we scale.”
To keep its IT team focused on value-added tasks, TVG uses Google Cloud managed services such as Cloud Bigtable, a highly scalable NoSQL database, as well as Cloud Storage for backups and Cloud Pub/Sub for real-time messaging between applications.
“We like the software-defined nature of Google Cloud Platform,” says Saeid Vafaeisefat, Vice President of IT, TVG Network. “The managed services are so easy to use. Google Cloud Platform even helps us mitigate and absorb distributed denial of service attacks with its global load balancing features, which we don’t pay extra for.”
TVG worked with SADA Systems, a Google Cloud Premier Partner, for consulting and deployment assistance. “SADA Systems helped us gain a deeper understanding of the advantages of Google Cloud Platform so we could make better decisions about how our application would perform and scale,” says Saeid. “They provided the facilitation, follow-up, and expert advice we needed to make our deployment a success.”
Scaling with 80% less work
With an active-active cloud architecture spanning multiple regions and the ability to conduct continuous, automated load tests, TVG no longer worries about downtime during major racing events—or any time, for that matter. Infrastructure and tools that used to be required to scale and provide resiliency are no longer needed, reducing CapEx. And with autoscaling replacing human intervention, accidental downtime is much less of a concern.
“With Google Cloud Platform, we can scale for major racing events with 80% less IT involvement and up to $500,000 annual CapEx savings,” says Tim. “Google Cloud Platform gives us faster deployment—we’re releasing new enhancements to our wagering site four times a week instead of every two months.”
More profitable user journeys
TVG’s success is being driven by ongoing modernization made possible in part by Google Cloud Platform, with TVG becoming the first U.S. operator to launch native iPhone and iPad apps. Lower latency means that stale data is never an issue, allowing customers to place bets faster and more confidently from anywhere they happen to be—which generates more revenue for TVG.
“Being on Google Cloud Platform has allowed us to develop our customer-facing channel faster while focusing on automated deployment and immutable infrastructure,” says Tim. “We have far greater confidence in our platform, and last year we broke records on our major race days while delivering an excellent customer experience.”
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