There’s never been a better time to talk about multicloud, and the Google Cloud Multicloud Mindset series on Twitter Spaces was created to do just that! This series takes place once every two weeks and features live conversations with top experts about the latest multicloud topics. You can join the 15-minute Q&A to ask your top questions and listen to episodes later offline for up to 30 days after we chat.

If you happened to miss our last few episodes, we recommend checking out our introduction blog to the series for what you missed. Let’s dive into our latest episodes, discussing the impact of open source and novel security challenges in multicloud environments.

Episode #5: ‘The intersection of open source and multicloud’

Open source technology has been an integral part of computing since its earliest era, predating even the birth of technology hubs like Silicon Valley. Open source projects have been responsible for giving us some of the most popular software in the world, such as Mozilla Firefox and the operating system Linux.

In the fifth episode, we sat down with Mike Coleman, Cloud Developer Advocate at Google Cloud, and took a closer look into the history of open source technologies, the role they play in a multicloud world, and the developer perspective on using these technologies to do their work.

The concept of multicloud anchors on the ability to run workloads across clouds and being able to pick the providers that are best suited for specific parts of workloads. Adopting open source technologies and languages empower companies to use the tools they need, regardless of cloud provider, without the fear of getting locked into a specific provider.

“As you think about moving across different environments, whether that be cloud to cloud, or developer desktop to ultimate destination, whether that be your data center or the cloud. Open source software allows you to do that…and multicloud is just an extension of that. This idea that I need to run the same software wherever I go.” — Mike Coleman, Cloud Developer Advocate at Google Cloud

If you’ve ever wanted a developer’s take on the impact of multicloud and the influence of open source in software development and digital transformation trends, you’ll want to tune into this episode.

You can access the full conversation on Twitter Spaces.

Episode #6: ‘Novel challenges in security with multicloud’

In the sixth episode of the series, we chatted with Dr. Anton Chuvakin, Security Advisor at Office of the CISO at Google Cloud, about how security leaders and architects are shifting away from traditional security models, which are increasingly insufficient for multicloud environments.

As more organizations adopt multicloud approaches, the question of how to maintain security in these complex environments and the increasing burden on SecOps teams is top of mind. As Dr. Chuvakin noted, the challenges in the cloud facing more traditional teams range from types of telemetry and logs to volumes and lack of clarity on detection use cases. However, these issues intensify when extended to include multiple clouds, where learning how to do something on one provider may be completely different on another.

“If you end up multicloud, you need to know public cloud and how it works at a better level than you would if you’re going to a single provider. Just like if you’re trying to repair three cars, you need to first learn how to repair cars. You need to have more cloud knowledge to do multicloud, not less. You need to have more powerful superpowers in the public cloud computing area because you can’t just learn one provider and call it a day.” — Dr. Anton Chuvakin, Security Advisor at Office of the CISO at Google Cloud

During the discussion, he offered three tips for tackling multicloud security:

1. Learn cloud more, not less if you’re going multicloud. Multicloud requires more cloud knowledge because you can’t learn a single provider and call it a day. You’ll need to understand the differences in order to be able to secure multiple cloud environments.
2. Focus on learning cloud identity management and how it compares to your traditional identity management service functions. Start with identifying the differences and similarities in what you see in one cloud and then continue with other clouds you use.
3. Explore where your threat areas change in cloud environments when you plan detection and response activities to understand if your detection is covered across clouds.

If your organization is embracing multicloud, this is a great episode to listen and learn more about cloud security, the primary considerations and challenges facing security teams, and some helpful best practices for thinking about security in multicloud environments.

We’ll be sharing the latest topics and episodes with you every month in this blog series. Until next time.

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.”

In 2013, long before the world was discussing clean energy and sustainable practices, two IIT Madras graduates — Swapnil Jain and Tarun Mehta — had an idea to develop India’s first-ever electrical scooter.

This was at a time when auto manufacturers were still focusing on fossil-fuel-driven vehicles and ‘eco-friendly’ mobility solutions were more a trendy alternative catering to a niche market.

The duo founded Ather Energy in 2013 and launched their first fully-electric scooter, the Ather S340, in Bengaluru in 2016. Since then, the company has released several new models into the market and is planning to expand to eight more cities by the end of the year.

To support the smooth running of their vehicles, lower costs, improve time to market, and create great customer experience, Ather turned to Google Cloud.

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Editor’s note: Google Cloud partner commercetools shares how modern technologies like composable commerce, cloud-native infrastructure and artificial intelligence/machine learning (AI/ML) will lead the way in business-to-business (B2B) digital commerce this year.

Digital commerce in B2B has been predicted as the next big thing for years; yet, at the start of COVID-19, 60% of B2B companies had zero or limited eCommerce capabilities. The pandemic accelerated digitization and eCommerce has finally taken off: As of February 2022, 65% of B2B companies offered eCommerce capabilities.

The behavior of B2B buyers is also changing: Consumer-like expectations are at the heart of successful B2B commerce, and this is how manufacturers, distributors and wholesalers will shape their customer experiences. Today, 73% of B2B buyers want a personalized business-to-consumer or B2C-like experience. 83% prefer ordering or paying through digital commerce and 72% are eager to purchase across channels.

With digital commerce dictating how B2Bs will grow in 2023 and beyond, what trends will spur digital transformations across this business model? Here’s what the team at commercetools expects to unfold in B2B eCommerce this year.

#1 B2B firms are switching to cloud-native, composable commerce

B2B players still plagued with manual processes and siloed backend systems will move away from monolithic platforms and choose composable commerce. In a nutshell, composability enables businesses to select best-of-breed components, such as search, cart or checkout, and “compose” them into a custom application.

B2B firms will modernize their commerce backend, interoperating siloed systems like Configure Price Quote solutions (CPQs) for sales and enterprise resource planning solutions (ERPs) for order entry with an API-first and composable commerce stack. They will also pivot from on-premise deployments to cloud-native architectures as the baseline for auto-scaling capabilities instead of pre-provisioning online capacity during traffic peaks. That way, B2Bs can customize customer-centric experiences to boost revenue while reducing the complexity and cost of in-house IT infrastructure, as well as gaining operational efficiencies

B2Bs will maximize the cross-section of composable commerce and cloud-native infrastructure by leveraging a commerce backend like commercetools Composable Commerce hosted on Google Cloud. This combined solution provides commercetools’ ready-to-use components built as microservices and exposed as APIs, such as product information management (PIM) and unified cart, integrated through the Google Cloud Marketplace.

#2 Strong focus on data quality and personalization

Focusing on data quality continues to be a big trend in 2023. B2B buyers expect product, pricing, inventory and shipping data points to be accurate across every touchpoint so they can make better purchasing decisions, such as when to order products and calculate quantities.

With so many data points to capture throughout the customer journey — product, inventory, pricing and customer data — we’ll see more B2B companies reorganizing their vast information pools to elevate customer experiences. They will pivot to modular and API-first solutions, plus flexible data models, so they can break data silos from legacy monolithic platforms and access such data when needed.

We also expect to see more customer analytics to unlock data on buyer behavior. By understanding what customers see, click and add to their shopping lists, B2B businesses get valuable insights into how buyers behave, using this data in the shopping journey according to product interests. That way, it’s possible to offer personalized experiences across touchpoints without hassle.

“It is important for B2B companies to look at their data as if it is one of their products; invest in its upkeep and integrity while finding ways to continuously improve it. Using advanced analytics powered by AI and ML to identify patterns from large amounts of data, B2B companies can activate insights into customer decision journeys to maintain loyalty, personalize experiences to improve satisfaction and boost revenue, while also finding ways to optimize costs. For example, with analytics, enterprises can streamline spend to focus on the highest-performing channels and reduce waste.” — Carrie Tharp, Google Cloud VP of Retail and Consumer

With data-driven tools coming into play like Google Cloud’s Discovery AI, Recommendations AI and Vision Product Search connected with composable commerce, B2B players can boost customer analytics to personalize experiences, improve customer satisfaction and reduce churn.

#3 The B2B customer experience will be redesigned

B2B players are taking a page out of the B2C playbook to elevate experiences throughout the customer journey. While intense work needs to happen in the backend commerce engine, B2B players will also redesign their digital frontends. That means boosting website performance, while mobile responsiveness and personalization will be at the forefront of these advanced digital initiatives.

More than ever, B2B companies are looking for digital storefronts delivered as progressive web applications (PWAs) for optimized performance and responsiveness across devices, as well as fast-loading and responsive experiences to boost your digital presence, SEO rankings and conversion rate. B2Bs can further streamline frontend development with solutions natively connecting to Google Cloud Marketplace, which supports a variety of storefront providers, including commercetools Frontend.

Leveraging Google Cloud’s unique capabilities, such as PWA web app development, Google Cloud Discovery AI solutions that include Retail Search and Vision API Product Search, among many others, B2B companies are well positioned to boost digital commerce in the years to come.

What’s next in 2023?

2022 was already a turbulent year; for better or worse, 2023 is expected to have a similar fate. For B2Bs, even the ones with tight budgets, investing in digital commerce can help future-proof businesses for whatever’s happening this year. To dive deeper into all predictions and insights by commercetools in collaboration with Google Cloud, read the guide Pivotal Trends and Predictions in B2B Digital Commerce in 2023.

At Google, we believe that when it comes to solving a problem as big and urgent as climate change, we get more done when we collaborate. From beekeepers in Germany to urban foresters in Los Angeles, we support the work of nonprofits, scientists, and organizations that are working to mitigate the impact of climate change globally, and increase communities’ resilience to its effects.

To this end, we are proud to launch the Climate Innovation Challenge, which will provide Google Cloud research credits to advance a better understanding of climate resilience and promising solutions to address urgent climate challenges. I’m excited about this launch. We need to get smarter about the inevitable impact of our changing climate and how it will reshape our lives, supply chains, and business. Sustainability is a business-critical agenda, and we need intelligent technologies, leadership, and collaboration to drive industry transformations and reach a net-zero world.

The innovation and scale required to solve the toughest climate challenges will come from technology. At Google Cloud, we’re working across industries to increase climate resilience, applying cloud technology to help solve key challenges in the fight against climate change. Nonprofits, scientists, and organizations will be key in developing new research and innovations that will help us better understand how we can accelerate action on climate.

Through the new program, individual climate researchers in higher education and not-for-profit research organizations can apply for Google Cloud credit grants of up to $100,000 to accelerate their projects with Google’s state-of-the-art, data analytics and artificial intelligence (AI) cloud services, from Google Earth Engine (EE) to Google Public Datasets like the one from the National Oceanographic and Atmospheric Administration (NOAA). We will work with specialist partners in environmental organizations, agriculture, and carbon reduction to help evaluate proposals and select participants. Our first partner is the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). Alongside the cloud credits, we will also provide researchers with access to technical training and mentoring, to help jumpstart their work.

From idea to insight to impact

In 2021, researchers at 500 universities in 47 countries received Google Cloud research credit grants. Others received funding through the Google Cloud Research Innovators Program, which promotes collaboration among a global cohort of scientists and provides them with professional opportunities and technical expertise. Here are some of the Research Innovators who have already advanced their climate research with Google Cloud:

• At CalTech Tapio Schneider and his team built Climate Machine, a next-generation open-source Earth System Model that will integrate more earth and atmospheric data than ever before.
• At UCLA, Bo Zhou uses EE for remote sensor modeling to help bureaus of land management make conservation decisions.
• At the University of New England, James Brinkhoff conducts spatial and temporal analysis for agriculture crop modeling and water use with data from EE.
• At Natural Resources Canada (NRCan), Richard Fernandes is developing the LEAF toolbox to map and assess vegetation with satellite data from EE.
• At the University of Toronto, Yuhong He uses EE to map changes occurring in natural and managed ecosystems systems using remote sensing, machine learning, and ecosystem modelings.
• At UCLA, Henry Houskeeper uses machine learning with EE’s satellite imagery to automate detection of kelp forests.
• At the University of Hawaii at Manoa, Jonghyun Lee conducts numerical modeling for water resources with Google Colab.
• At Technical University in Dublin, Santos Fernández Noguerol runs functions to collect weather data from governmental agencies, then automatically stores them in Google Cloud Storage buckets for future spatial analysis.
• At the University of Colorado at Denver, Farnoush Banaei-Kashani conducts data science projects with applications for Intelligent Transportation and Earth Sciences.

To apply for a Climate Innovation Challenge grant, click here and include “Climate Innovation Challenge” as the first line of your proposal. We will announce additional focus areas, partnerships, and recipients throughout the year. Click here to learn more about Google Cloud sustainability.