While there is uncertainty about how much the climate will change in the future, we know it won’t look like the past. Extreme weather events will increase in frequency and severity; the world will continue to warm, and the cost of climate change will increase.

Government plays a vital role in understanding and responding to these changes quickly. Achieving this improved response time will require data insights to ensure informed decision-making—from local to global scales. The challenge is not only urgent; it’s one of the world’s biggest “big data” problems.

Fortunately, new technologies to help us monitor the Earth are proliferating. Thousands of satellites take millions of images of the planet every day. Sensors generate data about temperature, precipitation, wind, soil conditions, and more—as frequently as every second. We have more information about the planet’s systems than at any other time in history. And the data will only continue to grow. The problem is not the lack of data–it is harnessing this data to drive insights for decision makers to tackle climate change. That’s why Google Cloud has partnered with Climate Engine.

How Climate Engine and Google Cloud enable greater climate resilience

Climate Engine is a scientist-led company that works with Google to accelerate and scale the use of Google Earth Engine’s world-class geospatial capacities (in addition to those of Google Cloud Storage and BigQuery, among other tools) in support of climate action in the public sector. Powered by Google Cloud’s infrastructure, Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities, enabling scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface. 

With cloud-based technologies, we can leverage massive computing at a scale that generates actionable insights from Earth-based data. These insights help us better manage resources, understand risks, predict changes, and respond to disasters as we meet the challenge of climate change. Geospatial AI combines the power of artificial intelligence (AI) and machine learning (ML) with geospatial analysis. Google Cloud’s Geospatial AI solutions provide departments and agencies with a centralized system to collect, process, and deliver Earth-based data into decision-making contexts.

Climate Engine and Google Cloud provide specialists with the opportunity to go back in time and see how our landscapes have changed due to changes in climate and other human activities over the past few decades. Years of data can now be quantitatively analyzed and visualized in a matter of a few seconds, enabling government agencies to fulfill their mandates by drawing invaluable insights into how landscapes are changing, what physical and natural assets are at risk, and where the opportunities are for reducing emissions and increasing carbon sequestration. 

“This is game changing for natural resource managers and scientists at public institutions at all levels of government,” says Dr. Daniel McEvoy, regional climatologist, at the Desert Research Institute & Western Regional Climate Center, Nevada System of Higher Education.https://www.youtube.com/embed/aPGsi8bd_Zk?enablejsapi=1&

Use cases for geospatial climate information systems

The use cases for this technology are as varied as the climate challenges themselves. These include monitoring, predicting, and analyzing the risks of extreme weather events like floods, wildfire, drought, extreme heat, wind, and other climate hazards. Use cases also include tracking changes in ecosystems, disease vectors, water availability and quality, soil health, growing seasons, air pollution, and more. These use cases are some of the ways that Google Cloud and Climate Engine can help the public sector deliver on government mandates. These provide insights that are helpful for a wide range of departments, and that can be applied in spatial and temporal scales that are meaningful for governments to take action.

“Our planet is changing at a rate that we have never experienced,” says Forrest Melton of the NASA Western Water Applications Office. To respond to these changes, we must understand what is happening across a wide range of environmental variables and at geospatial scales that range from local to global. We now have access to more data about the planet than ever before. The big challenge is converting data into actionable insights and then rapidly integrating these insights into decision-making systems. Climate Engine and Google Cloud help resolve this problem through innovative analytical tools and effective use of cloud computing.” 

Climate change carries an existential risk to our current and future stability and security. Together, we are working to provide transformational technologies that help meet that risk and build a safer, more resilient future for all of us. 

Learn more about Google Cloud’s environmental initiatives here and here.

For the past few years, we’ve applied core Google AI research and engineering to projects with positive societal impact, including forecasting floods, protecting whales and predicting famine. Artificial intelligence has incredible potential to address big social, humanitarian and environmental problems, but in order to achieve this potential, it needs to be accessible to organizations already making strides in these areas. So, the Google AI Impact Challenge, which kicked off in October 2018, was our open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges.

“Accelerating social good with artificial intelligence” sheds light on the range of organizations using AI to address big problems. It also identifies several trends around the opportunities and challenges related to using AI for social good. Here are some of the things  we learned—check out the report for more details.

AI is globally relevant 

We received 2,602 applications from six continents and 119 countries, with projects addressing a wide range of issue areas, from education to the environment. Some of the applicants had experience with AI, but 55 percent of not-for-profit organizations and 40 percent of for-profit social enterprises reported no prior experience with AI. 

Similar projects can benefit from shared resources

When we reviewed all the applications, we saw that many people are trying to tackle the same problems and are even using the same approaches to do so. For example, we received more than 30 applications proposing to use AI to identify and manage agricultural pests. The report includes a list of common project submissions, which will hopefully encourage people to collaborate and share resources with others working to solve similar problems.  

You don’t need to be an expert to use AI for social good

AI is becoming more accessible as new machine learning libraries and other open-source tools, such as Tensorflow and ML Kit, reduce the technical expertise required to implement AI. Organizations no longer need someone with a deep background in AI, and they don’t have to start from scratch. More than 70 percent of submissions, across all sectors and organization types, used existing AI frameworks to tackle their proposed challenge. 

Successful projects combine technical ability with sector expertise 

Few organizations had both the social sector and AI technical expertise to successfully design and implement their projects from start to finish. The most comprehensive applications established partnerships between nonprofits with deep sector expertise, and academic institutions or technology companies with technical experience.

ML isn’t the only answer 

Some problems can be addressed by using alternative methods to AI—and result in faster, simpler and cheaper execution. For example, several organizations proposed using machine learning to match underserved populations to legal knowledge and tools. While AI could be helpful, similar results could be achieved through a well-designed website. While we’ve seen the impact AI can have in solving big problems, you shouldn’t rule out more simple approaches as well. 

Global momentum around AI for social good is growing—and many organizations are already using AI to address a wide array of societal challenges. As more social sector organizations recognize AI’s potential, we all have a role to play in supporting their work for a better world. 

In our conversations with customers, few generative AI use cases have driven as much enthusiasm as generative search. Leaders at enterprises know the limits of traditional enterprise search, with queries producing a list of links based on pattern matching, and significant manual investigation required to find the more relevant answers. In generative AI, these leaders see an opportunity to leverage their data more effectively and deeply, including applying it to conversational apps that can answer complex questions, produce accurate summaries that synthesize many sources, and help people get the information they need, faster. 

Enterprise Search on Generative AI App Builder (Gen App Builder) lets organizations create custom chatbots and semantic search applications in as little as a few minutes, with minimal coding needed to get started and enterprise-grade management and security built in. Customers can combine their internal data with the power of Google’s search technologies and generative foundation models, delivering relevant, personalized search experiences for enterprise applications or consumer-facing websites. 

To date, most approaches to combining generative AI and search technology have been inadequate for the scale and reliability needed for enterprise use. For example, building search by breaking long documents into chunks and feeding each segment into an AI assistant typically isn’t scalable and doesn’t effectively provide insights across multiple sources. Likewise, many solutions are limited in the data types they can handle, prone to errors, and susceptible to data leakage. Bespoke, do-it-yourself approaches are generally no easier, with production-grade solutions often requiring complex tasks like integrating embeddings data with foundation models and significant use case data testing. Even when organizations make these efforts, the resulting solutions still tend to lack feature completeness and reliability, with significant investments of time and resources required to achieve high-quality results.

These challenges demonstrate that to effectively implement generative search, organizations typically need more than access to powerful foundation models. They also likely need the ability to ground model outputs in specific data, so that outputs are more relevant and less likely to include mistakes or “hallucinations.” They generally need safeguards that protect their data, how it is accessed, and how it is used. And they generally need the process to be high-performant and scalable out of the box, making the functionality easy to use even if the organization lacks data science and machine learning expertise.

Let’s look at how Enterprise Search on Gen App Builder helps customers bypass these scale and reliability challenges, so they can start leveraging generative search quickly. 

The intersection of generative AI and enterprise data 

Enterprise Search on Gen App Builder lets developers create search engines that help ground outputs in specific data sources for accuracy and relevance, can handle multimodal data such as images, and include controls over how answer summaries are generated. Multi-turn conversations are supported so that users can ask follow up questions as they peruse outputs, and customers have control over their data—including the ability to support HIPAA compliance for healthcare use cases. All of this is available as a fully managed service, so developers can focus on building rather than cloud complexity. 

Gen App Builder’s out-of-box capabilities can remove the need for data chunking, generating embeddings, or managing indexes, hiding complexity behind a straightforward interface that lets developers build apps in minutes with little or no coding and no prior machine learning experience. With the ability to ingest large volumes of documents and support for both unstructured and structured data, apps built with Gen App Builder help customers solve the long-standing headache of finding relevant information across the organization, turning tasks that used to take hours into quick searches or conversational explorations with an app. 

These capabilities are underpinned by both Google’s foundation models and a variety of Google Search technologies, including:

• Semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user’s query input. 
• Google’s understanding of how users search for information. 
• Google’s expertise in understanding relevance, which considers factors such as content popularity and user personalization of content when determining the order in which search results are displayed. 

For more advanced use cases, Gen App Builder can be easily integrated with Vertex AI for in-depth foundation model tuning, enabling flexible input and output search formats. 

As always with our AI products, Gen App Builder has been evaluated for alignment with our AI Principles, which is reflected by the product’s many safeguards against bias, toxic content, and unhelpful outputs. Whether for prototypes built in minutes or apps with many custom components, Enterprise Search on Gen App Builder offers a robust suite of user-friendly tools for customers across industries and levels of expertise. 

How customers are innovating with Enterprise Search 

After gaining access to Enterprise Search on Gen App Builder via our trusted tester program, a number of customers are already leveraging the product for novel use cases. 

Priceline is harnessing Gen App Builder and Vertex AI for a range of projects, including internal search engines for employees and a new chatbot to assist customers as they make travel plans. Slated to be available across both desktop and mobile experiences, Priceline’s chatbot will help customers find the right information faster via always-on, personalized experiences, including answering nuanced questions like, “What are the best 4-star hotel options in midtown Manhattan within walking distance to Central Park?” and “Can you help me extend my hotel reservation for an additional night?”

“Priceline is charting a course to transform the novelty of generative AI into lasting value for our customers and our business. We believe it’s not just about having the latest technology; it’s also about practically targeting innovation to the right challenges and opportunities,” said Marty Brodbeck, Chief Technology Officer, Priceline. “With Google Cloud as our AI innovation partner, we’re doubling down on our commitment to delivering the fastest, most seamless and informative booking experience for our customers, from personalized planning and travel inspiration to customer service.”

Vodafone is experimenting with Enterprise Search and foundation models on Gen App Builder to build a tool that can rapidly and securely query documents, search, and understand specific commercial terms and conditions. Vodafone Voice and Roaming Services has over 10,000 contracts with other telecommunications companies worldwide, in a variety of formats such as PDFs, images, and complex tables. Searching this document repository is often a time consuming process for employees. 

“Every day we introduce and manage new services like 5G to a roaming footprint comprising more than 700 operators in 210 countries to ensure both Vodafone and our partners’ business customers, holidaymakers, and Internet of Things devices stay connected when abroad. With Enterprise Search on Gen App Builder, we are building an intelligent assistant to securely and quickly search contracts.” said Sherif Bakir, CEO of Vodafone Voice and Roaming Services. “With generative AI, we’re accelerating otherwise protracted processes, increasing productivity and operational efficiency.”

Software startup Trender.ai, which creates customer monitoring and intelligence solutions for B2B relationships, is using Gen App Builder to build a product that can synthesize information from social media, public sources, and CRM data so that users can form more productive, personal relationships with prospects and customers.

“With Enterprise Search on Gen App Builder, we have been able to do things in the last month that we had projected to take 12-18 months on our prior roadmap,” said Betsy Bilhorn, co-founder at Trender.ai. “We had expected it would take at least 12 months to build and train models from an individual’s public social, web, and other data, and to then be able to ask questions like ‘What things are most important to this person?’ or ‘When and how is the best way to make an introduction to this prospect that they’ll respond to?’ Last year, we thought getting to this vision was a bit of a moonshot for a startup our size — but now we were able to achieve this in under a month.” 

Stop searching for solutions — start discovering insights

We’re excited to see how customers use Enterprise Search on Gen App Builder to leverage data in powerful ways, discover new insights, and create useful, personal, and efficient experiences. 

Today, we’re pleased to help our customers accomplish these goals, with the general availability of Enterprise Search on Gen App Builder for customers on the allowlist (i.e., approved for access). Please contact your Google Cloud sales team for access and pricing details. We are also launching two new features within Enterprise Search on Gen App Builder that are available today in our preview offering: multi-turn search, which supports asking follow-up questions, and content recommendations to find semantically relevant content. Access to the preview offering is available via Google Cloud’s trusted tester program. Read more about Enterprise Search on Gen App Builder and sign up for access on our webpage. 

We’re also bringing generative AI features of Enterprise Search to our existing solutions like Contact Center AI and Document AI. As an example, starting this month, customers can preview generative AI-powered search in Document AI Warehouse. To keep up with our latest generative AI news, don’t miss The Prompt or our generative AI primer for executives on Transform with Google Cloud.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job. Also, end to end use cases require Spark to be used along with technologies like TensorFlow, and programming languages like SQL and Python. Today, these operate in silos, with Spark on unstructured data lakes, SQL on data warehouses, and TensorFlow in completely separate machine learning platforms. This increases costs, reduces agility, and makes governance extremely hard; prohibiting enterprises from making insights available to the right users at the right time.

Announcing Spark on Google Cloud, now serverless and integrated

We are excited to announce Spark on Google Cloud, bringing industry’s first autoscaling serverless Spark, seamlessly integrated with the best of Google Cloud and open source tools, so you can effortlessly power ETL, data science, and data analytics use cases at scale. Google Cloud has been running large scale business critical Spark workloads for enterprise customers for 6+ years, using open source Spark in Dataproc. Today, we are furthering our commitment by enabling customers to:

1. Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
2. Enable data users of all levels: Connect, analyze, and execute Spark jobs from the interface of users’ choice including BigQuery, Vertex AI or Dataplex, in 2 clicks, without any custom integrations.
3. Retain flexibility of consumption: No one size fits all. Use Spark as serverless, deploy on Google Kubernetes Engine (GKE), or on compute clusters based on the requirements.

With Spark on Google Cloud, we are providing a way for customers to use Spark in a cloud native manner (serverless), and seamlessly with tools used by data engineers, data analysts, and data scientists for their use cases. These tools will help customers on their way to realize the data platform redesign they have embarked on.

“Deutsche Bank is using Spark for a variety of different use cases. Migrating to GCP and adopting Serverless Spark for Dataproc allows us to optimize our resource utilization and reduce manual effort so our engineering teams can focus on delivering data products for our business instead of managing infrastructure. At the same time we can retain the existing code base and knowhow of our engineers, thus boosting adoption and making the migration a seamless experience.”—Balaji Maragalla, Director Big Data Platform, Deutsche Bank

“We see serverless Spark playing a central role in our data strategy. Serverless Spark will provide an efficient, seamless solution for teams that aren’t familiar with big data technology or don’t need to bother with idiosyncrasies of Spark to solve their own processing needs. We’re excited about the serverless aspect of the offering, as well as the seamless integration with BigQuery, Vertex AI, Dataplex and other data services.” —Saral Jain, Director of Engineering, Infrastructure and Data, Snap Inc.

Dataproc Serverless for Spark

Per IDC, developers spend 40% time writing code, and 60% of the time tuning infrastructure and managing clusters. Furthermore, not all Spark developers are infrastructure experts, resulting in higher costs and productivity impact. With serverless Spark, developers can spend all their time on the code and logic. They do not need to manage clusters or tune infrastructure. They submit Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job. Furthermore, while Spark users today pay for the time the infrastructure is running, with serverless Spark they only pay for the job duration.

Spark through BigQuery

BigQuery, the leading data warehouse, now provides a unified interface for data analysts to write SQL or PySpark. The code is executed using serverless Spark seamlessly, without the need for infrastructure provisioning. BigQuery has been the pioneer for serverless data warehousing, and now supports serverless Spark for Spark-based analytics.

Spark through Vertex AI

Data scientists no longer need to go through custom integrations to use Spark with their notebooks. Through Vertex AI Workbench, they can connect to Spark with a single click, and do interactive development. With Vertex AI, Spark can easily be used together with other ML frameworks like TensorFlow, Pytorch, Sci-kit learn, and BigQuery ML. All the Google Cloud security, compliance, and IAM are automatically applied across Vertex AI and Spark. Once you are ready to deploy the ML models, the notebook can be executed as a Spark job in Dataproc, and scheduled as part of Vertex AI Pipelines.

Spark through Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale. Now, you can use Spark on distributed data natively through Dataplex. Dataplex provides a collaborative analytics interface, with 1-click access to SparkSQL, Notebooks, or PySpark, and the ability to save, share, search notebooks and scripts alongside data.

Flexibility of consumption

We understand one size does not fit all. Spark is available for consumption in 3 different ways based on your specific needs. For customers standardizing on Kubernetes for infrastructure management, run Spark on Google Kubernetes Engine (GKE) to improve resource utilization and simplify infrastructure management. For customers looking for Hadoop style infrastructure management, run Spark on Google Compute Engine (GCE). For customers, who’re looking for no-ops Spark deployment, use serverless Spark! 

ESG Senior Analyst Mike Leone commented, “Google Cloud is making Spark easier to use and more accessible to a wide range of users through a single, integrated platform. The ability to run Spark in a serverless manner, and through BigQuery and Vertex AI will create significant productivity improvement for customers. Further, Google’s focus on security and governance makes this Spark portfolio useful to all enterprises as they continue migrating to the Cloud.”

Getting started

Dataproc Serverless for Spark will be Generally Available within a few weeks. BigQuery and Dataplex integration is in Private Preview. Vertex AI workbench is available in Public Preview, you can get started here. For all capabilities, you can request for Preview access through this form.

You can work with Google Cloud partners to get started as well.

“We are excited to partner with Google Cloud as we look to provide our joint customers with the latest innovations on Spark. We see Spark being used for a variety of analytics and ML use cases. Google is taking Spark a step further by making it serverless, and available through BigQuery, Vertex AI and Dataplex for a wide spectrum of users.” —Sharad Kumar, Cloud First data and AI Lead at Accenture

For more information, visit our website or the watch announcement video and our conversation with Snap at Next 2021.

At Google I/O this year, we introduced Vertex AI to bring together all our ML offerings into a single environment that lets you build and manage the lifecycle of ML projects. In a previous post, we gave you an overview of Vertex AI, sharing how it supports your entire ML workflow—from data management all the way to predictions. Today, we’ll talk a little about how to manage ML datasets with Vertex AI.

Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can help. Vertex AI provides tools for every step of the machine learning workflow—from managing data sets to different ways of training the model, evaluating, deploying, and making predictions. It also supports varying levels of ML expertise, so you don’t need to be an ML expert to use Vertex AI.https://www.youtube.com/embed/CN2X6oIlnmI?enablejsapi=1&

Types of data you can use in Vertex AI

Datasets are the first step of the machine learning lifecycle—to get started you need data, and lots of it. Vertex AI currently supports managed datasets for four data types—image, tabular, text, and videos. 

Image

Image datasets let you do:

• Image classification—Identifying items within an image.
• Object detection—Identifying the location of an item in an image
• Image segmentation—Assigning labels to pixel level regions in an image.

To ensure your model performs well in production, use training images similar to what your users will send. For example, if users are likely to send low quality images, be sure to have blurry and low resolution images in your data set. Don’t forget to include different angles, backgrounds, and resolutions. We recommend you include at least 1,000 images per label (item you want to identify), but you can always get started with 10 per label. The more examples you provide, the better your model will be.

Tabular

Tabular datasets enable you to do:

• Regression—Predicting a numerical value.
• Classification—Predicting a category associated with a particular example.
• Forecasting—Predicting the likelihood of sudden events or demands.

Tabular data sets support hundreds of columns and millions of rows. 

Text

With text datasets, you can do:

• Classification—Assigning one or more labels to an entire document.
• Entity extraction—Identifying custom text entities within a document, like “too expensive” or “great value”.
• Sentiment analysis—Identifying the overall sentiment expressed in a block of text, for example, if a customer was happy or upset or frustrated.

Video

Video datasets enable:

• Classification—Labeling entire videos, shots, or frames.
• Action recognition—Identifying clips video clips where specific actions occur.
• Object tracking—Tracking specific objects in a video.

Creating and managing datasets in Vertex AI

Now that we’ve covered the different types of data you can use, let’s shift to creating and managing those datasets. In the Cloud Console, go to Vertex AI dashboard page and click Datasets, then click Create Project.

Say you want to classify items within a set of photos. Create an image dataset and select image classification. You can import files directly from your computer, which will be stored in Cloud Storage. Then, you’ll need to add the corresponding labels (items you want to identify) for your images. If you already have labels, you can use the Import File option to import a CSV with your image URLs and their labels. If your data is not labeled and you would like human help to label it, you can use the Vertex AI data labeling service. Once the files are uploaded, you can create labels and assign them to the images. You can also analyze the images in the data set, the number of images per label, and a few other properties. 

Depending on the type of data you use, your options might vary slightly. For example, if you want to use tabular data, you could upload a CSV file from your computer, use one from Cloud Storage, or select a table from BigQuery directly. Once you select the table, the data is available for analysis.

More to come

This concludes our overview of creating and managing datasets in Vertex AI. In a future installment, we’ll go over the next phase of the machine learning workflow: building and training ML models. 

If you enjoyed this post, keep an eye out for more AI Simplified episodes on YouTube. In the meantime, here’s where you can learn more about Vertex AI.