Editor’s note: Prevision.io has built the first ever pay-as-you-go AI management platform that simplifies the machine learning project lifecycle while offering powerful analytics capabilities. Now available exclusively on Google Cloud Marketplace, users can experiment, build, deploy, and manage AI projects in the cloud in weeks—without having extensive data science knowledge.

Similar to the momentum that cloud technology has had in the business world, AI and machine learning are quickly becoming essential to the enterprise. 86% of companies now view AI as a “mainstream technology,” and corporate AI adoption rose 50% in 2021 from the year prior for initiatives such as service-operations optimization and product enhancement. But for companies that don’t fall into the Fortune 500, initiating AI projects can come with several challenges, ranging from one-size-fits-all subscription models to skills gaps and resource-heavy monitoring requirements.

My team of data scientists saw a real need for software that could democratize machine learning innovation by removing these common barriers. We knew that features like automation could make building and deploying AI much more doable for other data scientists, as well as citizen data scientists. So, we launched Prevision.io, a first-of-its-kind dedicated AI management platform built on Google Cloud and now available exclusively on Google Cloud Marketplace.

As a Google Cloud Partner Advantage Member, we knew the benefits of an elastic infrastructure, full integration with BigQuery, and access to a large library of complementary tools, such as Kubeflow on Google Cloud. As a result, it’s easier for our users to improve upon their robust AI projects.

Set up in minutes, deploy a machine learning model in weeks

Users can start building and deploying models in Prevision.io immediately after subscribing through Google Cloud Marketplace. Instead of taking weeks to onboard and months to launch a real-world model into production, Prevision.io’s intuitive interface and powerful predictive analytics capability makes it possible to set up in minutes–and have models up and running in three to four weeks. Since we believe in cost-efficient scaling, our platform operates on a pay-as-you-go model with no long-term contracts, licensing, or per-user fees.

Simply connect Prevision.io with your data–whether it exists in buckets or in an SQL data source like BigQuery. No matter your data source, set up is quick. There are even tutorials to help if you’re using APIs. Follow this tutorial for step-by-step set up details in Google Cloud.

Once historical data is imported, you can start applying your own models inside Prevision.io, or use Prevision.io to build a bespoke model. There’s more good news: expenditures on Prevision.io’s platform are applied toward customers’ Google Cloud spend commitments.

Full lifecycle AI project management made easy

By automating the complexity of AI project management with a no-code approach, businesses do not have to add more data scientists to their teams, risk data drift or outdated models, spend hundreds of thousands on unused software, or massively expand IT budgets. By connecting BigQuery or other datastores to Prevision.io, you can launch high-performing machine learning projects and manage them across the entire lifecycle. With Prevision.io you can:

• Experiment with iteration and optimization to get an effective model into production from the start. Track performance and compare versions to identify the most reliable model. Because there is no infrastructure to manage, users can focus only on the project and see ROI sooner.
• Automate training and prediction tasks to improve collaboration, reduce time-consuming manual operations, and boost results. Users can implement automation across the production pipeline with built-in features like AutoML and a scheduler for recurring tasks. Retrain automations and integrate custom code to keep processes aligned and relevant.
• Deploy scalable working models securely and reliably in one-click. Tailor deployments using REST APIs or as a component to generate batch predictions. Create dashboards to share with stakeholders, and swiftly update your model without worrying about service interruptions or breakages.
• Monitor infrastructure and model behavior to understand resource utilization and how data changes over time—without requiring more of IT. Put an end to endless maintenance meetings with reliable, real-time monitoring applications around drift, data in-and-out, and prediction distribution. If an issue arises, Prevision.io provides detailed alerts and analysis to understand the root of a problem.

Any business can benefit

Regardless of industry or department, we’ve seen Prevision.io help businesses solve some of their biggest challenges. Utilities companies are relying on better forecasts of the energy consumption (gas or electricity).. Transportation companies have deployed machine learning models that can inform logistical operations based on fluctuating supply and demand. Doing more with data not only improves what a business can offer their customers but can also yield significant savings. Here are a few real-life examples:

La Poste: delivery data saves the day
Global delivery company La Poste was having trouble meeting customers’ demand for speed and visibility, and inaccurate estimated arrival times for packages was costing them money. The team wanted to put its tracking and tracing data to work, and turned to Prevision.io to select technical metrics, set up personalized machine learning models for delivery rounds of all personnel, and speed up the iteration and training process. After deploying its model in four weeks, La Poste achieved an 89% accuracy rate for delivery times and saw a 10x improvement in IT infrastructure and operational costs. And of course, happier customers who keep coming back.

BPCE: machine learning helps us help our clients
An arm of BPCE Group, the second largest banking group in France, was feeling the effects of the pandemic’s impact on customers. It needed a more efficient way of determining who would need what type of help—and when–to reduce the number of customers entering the collections process. Using its wealth of data to create and deploy a machine learning model in Prevision.io, the firm was able to rapidly identify the most at-risk customers and better understand the root causes behind potential debt default—some of which are easily fixable. As a result, the firm has seen a fifteen-fold increase in the sums they have been able to recover, and decreased the number of collection cases by 50%.

Pharmaceutical company: marketing medicine with MLOps
The marketing department at a healthcare company serving pharmacies was able to reduce customer churn and improve growth by using Prevision.io to compute market segmentation based on anticipated customer revenue. This helped the company determine the best way to engage with each pharmacy—and when. Being able to make strategic decisions based on automated predictions and more targeted data saved the company $1.3M Euros in two fiscal quarters.

See how AI project management and MLOps made easy can transform your business. Access Prevision.io on Google Cloud Marketplace and take advantage of the 14-day free trial.

When I was growing up in Thailand, I witnessed the challenges facing farmers including rising food demand, shortage of labor, and uneven crop yields caused by climate change. As a result, many smallholder farmers found themselves trapped in a vicious circle, unable to reduce food insecurity due to low yields, but lacking the resources to invest for a more profitable future.

I was determined to make a difference. After earning my master’s degree in Information Management I joined a research project with the University of Tokyo where we used sensors to monitor spinach fields in Thailand. The results of this early experiment in precision farming were impressive. We proved that it was possible to grow organic crops with minimal use of fertilizers, while consumers benefited from higher quality spinach grown in Thailand.  

This inspired me to found ListenField in 2017. Our mission is to transform farm management by collecting data from multiple sources, including field sensors, soil scanning, weather data, seasonal forecasts, and satellite imagery. By modeling this data, we provide farmers with insights that enable them to optimize production ‘from soil to harvest’.

A bumper crop of farming data

Artificial intelligence and machine learning play a central role in our prediction platform, combining crop health monitoring, growth prediction, and soil nutrition analysis. Farmers can apply real-time insights to their schedule from our FarmAI Mobile App, while our FarmAI Dashboard enables agri-food businesses to collaborate with agronomists and farmers so that all parties benefit from higher profit margins and more sustainable growing strategies.

Another important feature of our business model is AgroAPI, which makes our analytics available to third parties. Clients can embed deep analytics in their applications, including crop growth prediction and remote sensing analysis, without needing to develop complicated algorithms and data pipelines by themselves.

We are also excited about our research into genomic prediction in collaboration with the Japanese government and several research companies. Using our Data-Driven Breeding Platform, breeders and seed companies can upload their genomic data and gain practical insights that help accelerate the reproduction of high-quality seeds and plants.

Today, more than 30,000 farmers use our technology especially in Vietnam and Thailand where it is used to improve rice, cassava, and sugar cane yields. The technology is also being rolled out for orange and mango farmers enabling them to monitor individual trees and adjust irrigation to improve the sweetness of the fruit at harvest time.

Responding fast to changing conditions

We were using another cloud provider to run our business, but one of the ListenField team members drew our attention to Google Cloud. As well as the technology, we were also attracted by the Google for Startups Cloud Program which provides us with Google Cloud credits that cover our first and second years of Google Cloud usage. We also met our Account Representative, who provides us with training, business and tech support, and Google-wide discounts. It’s great to have a point of contact that can help us on our startup journey and make the most of Google’s resources.

By reducing the pressure on our finances and human resources, we were able to experiment and adapt in response to early experiments. This flexibility also enabled us to demonstrate a compelling business case to new and existing investors.

Our Google Cloud Platform Partner, Navagis, gave us additional momentum thanks to their expertise in mapping and geospatial data. They also played a crucial role in the integration of Google Earth Engine, which we use to map agricultural areas.

We also use Firebase for application development and Google Workspace for team collaboration. Colab and Vertex AI enable us to build, deploy, and scale our machine learning models quickly, ensuring that we remain competitive and attractive to new customers.

Giving female entrepreneurs the opportunity to flourish

Both the Google Cloud team and our colleagues at Navagis helped us to navigate the challenges many early-stage startups face. My background is in science and academia, so I appreciated the business mindset offered by both organizations to help us continue to grow and scale.

Being a female entrepreneur leading a startup can also be tough, but Google Cloud and Navagis helped me to build a strong network, access funding, and make my voice heard. Today, ListenField has several female executives, while 50% of our researchers and many of the farmers on our platform are women.  

Above all, Google Cloud helps us to power an agriculture revolution in south-east Asia. Smallholder farmers can transition from analog to digital farming, improving their yields and reducing waste. The benefits to the economy are also significant including greater food security and reducing the use of industrial fertilizers that generate potent greenhouse gasses.

And that’s just the beginning of what we can do. Our next milestone is to reach 50,000 farmers and cut one million tonnes of greenhouse gas emissions. It sounds ambitious, but with the Google Cloud and Navagis teams behind us, I’m confident that we will reach these targets.

ListenField team members

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Documents are indispensable parts of our professional and personal lives. They give us crucial insights that help us become more efficient, that organize and optimize information, and that even help us to stay competitive. But as documents become increasingly complex, and as the variety of document types continues to expand, it has become increasingly challenging for people and businesses to sift through the ocean of bits and bytes in order to extract actionable insights.

This is where Google Cloud’s Document AI comes in. It is a unified, AI-powered suite for understanding and organizing documents. Document AI consists of Document AI Workbench (state-of-the-art custom ML platform), Document AI Warehouse (managed service with document storage and analytics capabilities), and a rich set of pre-trained document processors. Underpinning these services is the ability to extract text accurately from various types of documents with a world-class Document Optical Character Recognition (OCR) engine.

Google Cloud’s Document AI OCR takes an unstructured document as input and extracts text and layout (e.g., paragraphs, lines, etc.) from the document. Covering over 200 languages, Document AI OCR is powered by state-of-the-art machine learning models developed by Google Cloud and Google Research teams.

Today, we are pleased to announce three new OCR features in Public Preview that can further enhance your document processing workflows.

1. Assess page-level quality of documents with Intelligent Document Quality (IDQ)

With Document AI OCR, Google Cloud customers and partners can programmatically extract key document characteristics – word frequency distributions, relative positioning of line items, dominant language of the input document, etc. – as critical inputs to their downstream business logic. Today, we are adding another important document assessment signal to this toolbox: Intelligent Document Quality (IDQ) scores.

IDQ provides page-level quality metrics in the following eight dimensions:

1. Blurriness
2. Level of optical noise
3. Darkness
4. Faintness
5. Presence of smaller-than-usual fonts
6. Document getting cut off
7. Text spans getting cut off
8. Glares due to lighting conditions

Being able to discern the optical quality of documents helps assess which documents must be processed differently based on their quality, making the overall document processing pipeline more efficient. For example, Gary Lewis, Managing Director of lending and deposit solutions at Jack Henry, noted, “Google’s Document AI technology, enriched with Intelligent Document Quality (IDQ) signals, will help businesses to automate the data capture of invoices and payments when sending to our factoring customers for purchasing. This creates internal efficiencies, reduces risk for the factor/lender, and gets financing into the hands of cash-constrained businesses quickly.”

Overall, document quality metrics pave the way for more intelligent routing of documents for downstream analytics. The reference workflow below uses document quality scores to split and classify documents before sending them to either the pre-built Form Parser (in the case of high document quality) or a Custom Document Extractor trained specifically on lower-quality datasets.

2. Process digital PDF documents with confidence with built-in digital PDF support

The PDF format is popular in various business applications such as procurement (invoices, purchase orders), lending (W-2 forms, paystubs), and contracts (leasing or mortgage agreements). PDF documents can be image-based (e.g., a scanned driver’s license) or digital, where you can hover over, highlight, and copy/paste embedded text in a PDF document the same way as you interact with a text file such as Google Doc or Microsoft Word.

We are happy to announce digital PDF support in Document AI OCR. The digital PDF feature extracts text and symbols exactly as they appear in the source documents, therefore making our OCR engine highly performant in complex visual scenarios such as rotated texts, extreme font sizes and/or styles, or partially hidden text.

Discussing the importance and prevalence of PDF documents in banking and finance (e.g., bank statements, mortgage agreements, etc.), Ritesh Biswas, Director, Google Cloud Practice at PwC, said, “The Document AI OCR solution from Google Cloud, especially its support for digital PDF input formats, has enabled PwC to bring digital transformation to the global financial services industry.”

3. “Freeze” model characteristics with OCR versioning

As a fully managed cloud-based service, Document AI OCR regularly upgrades the underlying AI/ML models to maintain its world-class accuracy across over 200 languages and scripts. These model upgrades, while providing new features and enhancements, may occasionally lead to changes in OCR behavior compared to an earlier version.

Today, we are launching OCR versioning, which enables users to pin to a historical OCR model behavior. The “frozen” model versions, in turn, give our customers and partners peace of mind, ensuring consistent OCR behavior. For industries with rigorous compliance requirements, this update also helps maintain the same model version, thus minimizing the need and effort to recertify stacks between releases. According to Jaga Kathirvel, Senior Principal Architect at Mr. Cooper, “Having consistent OCR behavior is mission-critical to our business workflows. We value Google Cloud’s OCR versioning capability that enables our products to pin to a specific OCR version for an extended period of time.”

With OCR versioning, you have the full flexibility to select the versioning option that best fits your business needs.

Getting Started on Document AI OCR

Learn more about the new OCR features and tutorials in the Document AI Documentation or try it directly in your browser (no coding required). For more details on what’s new with Document AI, don’t forget to check out our breakout session from Google Cloud Next 2022.

Communication service providers (CSPs) are at an inflection point. From stagnating revenues, to network strain in meeting the demands of 5G, to challenges in delivering innovative customer experiences, there’s enormous pressure on the telecommunications industry to transform.

Over the past few years, CSPs around the globe have turned to artificial intelligence (AI) to address some of these challenges, but the lion’s share of an operator’s operational expenses are still spent on infrastructure and data management. This has limited their ability to capitalize on core data assets and develop differentiated customer experiences that meet individual needs. 

Enter generative AI, a type of machine intelligence that’s received enormous attention as of late. We’ve all marveled over its ability to generate text that reads like it was written by a person, to create new images, and to build even musical scores. It’s a riveting addition to the AI toolset — and one that complements machine learning (ML) and its ability to identify patterns to make predictions, spot efficiencies, or interpret large data sets. 

But while there’s a lot of hype around generative AI, at Google Cloud we view it through a much more practical lens for the telecommunications industry. Generative AI can accelerate the transformation already underway, with its potential to streamline many of the tools and processes that CSPs engage with daily, bringing a new level of natural interaction between people and computers, and enabling machines to be programmed to carry out an action with a spoken request, and respond in natural, interactive ways. 

Generative AI builds on existing Google Cloud data, AI, and ML services. For example, Contact Center AI, with human-like interactions between callers and computers, has been successfully adopted by CSPs for many years, increasing the satisfaction of both customers and call center workers. As we add generative AI to this technology, CSPs and their customers will see even greater capabilities and impact, for example, virtual agents that not only provide helpful information, but also let customers make payments and execute other transactions. With generative AI, CSPs will be able to harness customer call summaries to better understand customer sentiment and identify cross-sell and up-sell opportunities. CSPs could also easily and quickly build and deploy virtual agents informed by customer conversations that enable more innovative and personalized customer interactions. And that’s just the start.

Three key areas

The contact center is just one of the areas where practical and generative AI will help drive new value. When reflecting on the key challenges facing CSPs today, three areas in particular stand out where generative AI may be transformative:

1. Personalized experiences: In addition to further improvements in customer call center interactions, generative AI can deliver improved personalization in ecommerce interactions — a big factor in helping customers sort through their choices of phones and calling plans. Personalization is also important for lowering churn, offering relevant new services, and managing the customer lifecycle. For example, generative AI could enable CSPs to produce marketing campaign content customized for select themes, and target individual customers with customized text and images. 
2. Autonomous networks: Generative AI will also help to pave the way for autonomous networks by connecting multiple complex AI/ML models used across network planning and operations with large language models (LLMs) that can understand network behaviors and create action plans in areas including network capacity planning and performance. For example, generative AI will enable CSPs to train models with customer experience and sentiment data to build better prediction capabilities. Importantly, the customer data sets used to tune these models are not public, but curated internal customer data — significantly enhancing privacy, factuality, and relevance, while protecting intellectual property. In addition, generative AI will be able to help network planning and design, which requires high levels of reporting and analysis.
3. Streamlined operations: Both operations center uptime and field service efficiency are crucial for managing costs and improving customer satisfaction. In particular, applying generative AI to field service devices can speed up diagnostics and analysis, and can even help with installation, parts, and troubleshooting, and minimizing the number of times companies have to send out trucks and improve field-service training. Generative AI will also provide a productivity boost to IT development processes, enabling code generation and troubleshooting to deliver reliable software products and services.

Data security and reliability

An under-discussed area in generative AI is the importance of data quality and data security in building and training the LLMs that power the technology. Many CSPs are rightly concerned about intellectual property leaking both into and out of LLMs, risking the security of their systems and their intellectual property. We’ve long offered industry-leading data security and privacy technologies, and with generative AI integrated with Vertex AI, we can ensure all data is secured within the CSP’s environment.

Meanwhile, to ensure that their LLMs generate accurate information, CSPs are building out scenarios and use cases for training on smaller, controlled amounts of their own data, sometimes accompanied by highly trusted sources from partners and others. Google Cloud also provides tools including Prompt Engineering, Tuning, and Reinforcement Learning from Human Feedback to further ensure data factuality and reliability. This will likely result in the first generative AI applications targeted to smaller, high-impact problems, like optimizing network topologies. 

The human element

Of course, people are critical to the success of generative AI, whether that means solving problems in call centers, field service workers combining AI information with their own knowhow, marketing and creative teams brainstorming with generative AI to make new presentations and marketing materials, or operations engineers augmenting and approving an AI suggestion. We’ve built a lot of amazing technology to help augment what people cannot do: synthesize millions, if not billions, of records and sources to help energize new workflows and productivity.

Telecommunications is a fast-changing industry that is tech savvy and hungry to learn and deploy the best possible new technologies, and that includes generative AI. Each meeting with a CSP inspires new ideas, sparks more use cases, and leads to more industry-changing initiatives. It’s exciting to see this pace of change, and we are only just getting started.

We look forward to showing you more soon as we deep dive into some of the exciting CSP industry use cases in upcoming blog posts. We also invite you to find out more about how Google Cloud is partnering with CSPs around the world to deliver a holistic cloud transformation.