At Google Cloud, we’re committed to making artificial intelligence (AI) accessible to everyone and easier to harness for new use cases.  That’s why we’re excited to announce the general availability of our intuitive, new visual user interface for Google Cloud’s Speech-to-Text (STT) API, right in Google Cloud Console, which makes the API much simpler and easier for developers to use.  

The STT API lets developers convert speech into text by leveraging Google’s years of research in automatic speech recognition and transcription technology. As advancements in AI continue to bring speech to new interfaces and devices, the STT API helps developers add speech functionality to their applications in order to better meet consumer demands. 

The STT API covers a wide variety of use cases, from dictation and short commands, to captioning and subtitles. Getting the most of STT, however, can be a complicated process. To achieve the highest accuracy on any AI use case requires careful testing and tuning. 

Previously, developers building on the STT API had to do this work manually by carefully experimenting with our API. Just to get started, developers needed familiarity with GCP integration concepts and had to either build their own tools or manage various scripts and API calls to fully understand the API documentation.  These actions required cumbersome and time-consuming effort and made measuring, customizing, and improving models even more difficult.  

Today’s announcement significantly simplifies the process, facilitating iteration and integration of models into developers’ applications by letting developers perform every API function from within the Google Cloud Console.  These tools will make it easier for developers to integrate the STT API with their products or services. This update also gives developers the ability to manage and quickly iterate on their STT model customizations with Model Adaptation.

Model Adaptation allows developers to customize STT specifically for their domains or use cases. Developers can maintain lists of words and weights that will be applied to either every request or just single requests, depending on their needs. Model adaptations are reusable and composable, so once developers have seen good results in the STT Cloud Console, they can deploy to their entire solution. 

The Speech-to-Text Cloud Console and Model Adaptation API is available now in all Google Cloud regions and languages and is accessible to all GCP users with no additional cost to that of the underlying API usage. The STT API supports over 70 languages in 120 different local variants. If you’re a developer looking for an easy to use, easy to integrate, and high-quality STT experience, sign up for our free trial and try our new interface on your own datasets today!

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.”

For years, Google has been committed to maximizing developer productivity. In 2019, we announced the general availability of Skaffold, a command-line tool that facilitates continuous development and delivery for containerized applications. Today, we’re excited to announce that Skaffold V2 is now generally available.

Skaffold V2 expands Skaffold’s supported platforms and architectures with the introduction of Cloud Run as a supported deployer, and now supports building from and deploying to both ARM and x86 architectures. Skaffold V2 also offers enhanced support for CI/CD and GitOps workflows, with the introduction of the skaffold render phase, verify phase, and kpt integration. Best of all, all existing Skaffold configurations are fully compatible with Skaffold V2, and upgrading from V1 is as easy as running skaffold fix.

Expanded platform support

Since its inception, Skaffold has supported deploying applications to Kubernetes, using either kubectl or Helm deployers. Deploying to Kubernetes with Skaffold unlocks the benefits of improved velocity from source to prod, with reusable building blocks for iterative development and CI/CD.

We’re excited to expand these benefits to Cloud Run, Google’s serverless container runtime. Cloud Run provides a fully managed platform for any containerized application, and includes features such as automatic resource scaling and integrated storage, security, and monitoring solutions.

It’s easy to get started with Skaffold and Cloud Run; all you need is a Cloud Run service config and a few small updates to your skaffold.yaml. Skaffold also powers Cloud Deploy’s support of Cloud Run. Check out our documentation to learn more.

In addition to the new deployment target, the expanded set of compatible image-architecture configurations with Skaffold V2 helps developers ensure that the architecture of the machine on which an image is built is compatible with the architecture of the machine on which the image is intended to be run. Skaffold now intelligently checks the architecture of your local machine as well as the target Kubernetes cluster before building your images, allowing you to deploy to ARM, x86 or multi-arch clusters from a x86 or ARM machine without any manual configuration.

Check out our documentation to learn more about deploying to Cloud Run and managing ARM workloads.

CI/CD and DevOps, simplified

Skaffold helps developers implement CI/CD and DevOps workflows by providing a set of reusable building blocks for repeatable build, tag, and deploy steps. With Skaffold, the same config can be shared in development and production, leveraging Skaffold profiles to implement environment-specific configuration.

With Skaffold V2, the Skaffold render phase is now distinct from the deploy phase. The output of the Skaffold render phase is a manifest, hydrated with tagged image names and templated values, which can then be persisted in source control before deployment as part of a GitOps workflow.

In addition to the render phase, Skaffold V2’s new verify phase helps to configure post-deployment tests. This phase can be used to configure a series of test containers that are then monitored to ensure that the deployment was successful. This allows developers to integrate this verification step into reusable deployment pipelines rather than running these tests manually.

Finally, the introduction of kpt as a supported renderer in Skaffold V2 provides a sophisticated syntax for serially transforming and validating your manifests, unlocking additional customizability and verification in your GitOps workflows. Using Skaffold makes it easy to adopt kpt because you can take advantage of kpt’s transformation and validation functionality without needing to write any separate kpt configuration. It’s as easy as adding a few stanzas to your existing skaffold.yaml. Kpt can also be used alongside Skaffold’s pre-existing integrations with renderers Helm and Kustomize.

Check out our documentation to learn more about the Skaffold render phase, verify phase, and kpt integration.

Upgrading to Skaffold V2

Getting started with Skaffold V2 is easy. If you’re new to Skaffold, check out the V2 installation guide for platform-specific installation instructions.

If you’re an existing Skaffold user, upgrading to Skaffold V2 is simple and requires no manual configuration changes. All of your existing Skaffold configurations will continue to work as-is with Skaffold V2. Simply download the Skaffold V2 binary and run skaffold fix to update your config. Check out the V2 upgrade guide for more details.

Finally, check out our documentation for more detailed instructions on taking advantage of all of the new features introduced in Skaffold V2.

What’s next?

If you’re interested in harnessing the power of Skaffold for serverless workloads, check out our documentation for using Skaffold V2 with Cloud Run.

Also, be sure to check out Cloud Deploy, Google’s fully managed continuous delivery offering, which leverages Skaffold to construct reusable deployment pipelines.

We’re excited to hear from you. As always, you can reach out to us on GitHub and Slack.

Application modernization is quickly becoming one of the pillars of successful digital transformation and cloud migration initiatives. Many organizations are becoming aware of the dramatic benefits that can be achieved by moving legacy, on-premises apps and databases into cloud native infrastructure and services, such as reduced Total Cost of Ownership (TCO), elimination of expensive commercial software licenses, and improved performance, scalability, security and availability.

The complexity of applications and databases to a cloud-centric architecture requires a rapid, accurate, and customized assessment of modernization potential and identification of challenges. Addressing business and functional drivers, TCO calculations, uncovering technological challenges and cross-platform incompatibilities, preparation of migration, and rollback plans can be essential to the success and outcome of the migration. 

These cloud migration initiatives are often divided into three high-level phases: 

1. Discovery: identifying and cataloging the source inventory. Output is usually an inventory of source apps, databases, servers, networking, storage, etc. The discovery of existing assets within a data center is usually straightforward and can often be highly automated. 
2. Pre-migration readiness: the planning phase. This includes the analysis of the current portfolio of the databases and applications for migration readiness, determining the target architecture, identifying technological challenges or incompatibilities, calculating TCO, and preparing detailed migration plans. 
3. Migration execution: where the rubber hits the road. During this phase of the migration process, database schemas are actively converted, the application data access layer is refactored, data is replicated from source to target, often in real-time, and the application is deployed in its determined compute platform(s). 

Successful evaluation and planning phase as part of the pre-migration readiness phase can bolster confidence in investment towards modernization. Skipping or inaccurately completing the pre-migration phase can lead to a costly and sub-optimal result. Relying on manual pre-migration assessments can lead to long migration timelines, reduced success rates and poor confidence in the post-migration state, increased risk and total migration cost.

Some of the commonly asked question during pre-migration include:

1. How compatible are my source databases, which are often commercial and proprietary in nature, with their open-source cloud-native alternatives? For example, how compatible are my Oracle workloads and usage patterns with Cloud SQL for PostgreSQL?
2. What’s my degree of vendor lock-in with my current technology stack? Are proprietary features and capabilities being used that are incompatible with open-source database technologies?
3. How tightly-coupled are my applications with my current database engine technology? Can my applications be deployed as-is, refactored for cloud readiness with ease, or will it be a big undertaking?
4. How much effort will my migration require? How expensive will it be? What will be my run-rate in Google Cloud post-migration and my ROI?
5. Can we identify quick-win applications and databases to start with?

There is a direct association between the accuracy and speed of the pre-migration phase and the outcome of the migration itself. The faster and more accurately organizations complete the required pre-migration analysis, the more cost efficient and successful the migration itself will usually be.  

EPAM Systems, Inc., a leader in digital transformation, worked with Google Cloud as a preferred partner to accelerate cloud migrations beginning with pre-migration assessments. Leveraging EPAM’s migVisor for Google Cloud—a unique pre-migration accelerator that automates the pre-migration process—and EPAM’s consulting and support services, organizations can quickly generate a cloud migration roadmap for rapid and systematic pre-migration analysis. This approach has resulted in the completion of thousands of database assessments for hundreds of customers.

migVisor is agentless, non-intrusive, and hosted in the EPAM cloud. migVisor seamlessly connects to your source databases and runs SQL queries to ascertain the database configuration, code, schema objects and infrastructure setup. Scanning of source databases is done rapidly and without interruption to production workloads.

migVisor prepares customers to land applications in Google Cloud and its managed suite of databases services and platforms such as Cloud SQL, bare metal hosting, Spanner and Cloud Bigtable. migVisor supports re-hosting (lift-and-shift), re-platforming, and re-factoring.  

“EPAM’s recent application assessment update to its migration tooling system, migVisor, will bring a new level of transparency to the entire application and database modernization process”,  said Dan Sandlin, Google Cloud Data GTM Director at Google Cloud. “This enables organizations to make the most of digital technologies and provides a clear IT ecosystem transformation that allows our customers to build a flexible foundation for future innovation.”

Previously, migVisor focused on assessments of the source databases and the compatibility of customers’ existing database portfolio with cloud-centric database technologies. Coming this quarter, migVisor adds support for application assessments, augmenting its existing and class-leading capabilities in the database space. 

The addition of application modernization assessment functionality in migVisor, combined with EPAM’s certification and specialization in Google Cloud Data Management and hands-on engineering experience, strengthens EPAM’s position as a leader for large-scale digital transformation projects and migVisor as a trusted product for cloud migration assessments to Google Cloud customers. EPAM provides customers an end-to-end solution for faster and more cost-effective migrations.  Assessments that used to take weeks can now be completed in mere days. 

Within minutes of registering for an account, anyone can start using migVisor by EPAM to automatically assess applications and application code. Visit the migVisor page to learn more and sign up for your account.