Cloud

2024 has been a landmark year for Google Earth Engine, marked by significant advancements in platform management, cloud integration, and core functionality. With increased interoperability between Google Cloud tools and services, and Earth Engine, we’ve unlocked powerful new workflows and use cases for our users.  Here’s a round up of this year’s top Earth Engine launches, many of which were highlighted in our Geo for Good 2024 summit.

Management: Streamlining Workflows

Earlier this year, we launched the new Earth Engine Overview page in the Cloud Console, serving as a centralized hub for Earth Engine resources, allowing you to manage Earth Engine from the same console used to manage and monitor other Cloud services. 

In this console, we also introduced a new Tasks page, allowing you to view and monitor Earth Engine export and import tasks alongside usage management and billing. The Tasks page provides a useful set of fields for each task, including state, runtime, and priority. Task cancellation is also easier than ever with single or bulk task cancellation in this new interface. 

As we deepen Earth Engine’s interoperability across Google Cloud, we’ll be adding more information and controls to the Cloud Console so that you can further centralize the management of Earth Engine alongside other services.

Integrations: deepening cloud interoperability

Earth Engine users can integrate with a number of cloud services and tools to enable advanced solutions requiring custom machine learning and robust data analytics. This year, we launched a set of features that improved existing interoperability, making it easier to both enable and deploy these solutions.  

Vertex AI integration
Using Earth Engine with Vertex AI enables use cases that require deep learning, such as crop classification. You can host a model in Vertex AI and get predictions from within the Earth Engine Code Editor. This year, we announced a major performance improvement to our Vertex Preview connector, which will give you more reliability and more throughput than the current Vertex connector.

Earth Engine access
To ensure all Earth Engine users can take advantage of these new integration improvements and management features, we’ve also transitioned all Earth Engine users to Cloud projects. With this change, all Earth Engine users can now leverage the power and flexibility of Google Cloud’s infrastructure, security, and growing ecosystem of tools to drive forward the science, research, and operational decision making required to make the world a better place.

Security: enhancing control

This year we launched Earth Engine support for VPC Service Controls – a key security feature that allows organizations to define a security perimeter around their Google Cloud resources. This new integration, available to customers with professional and premium plans, provides enhanced control over data, and helps prevent unauthorized access and data exfiltration. With VPC-SC, customers can now set granular access policies, restrict data access to authorized networks and users, and monitor and audit data flows, ensuring compliance with internal security policies and external regulations.

Platform: improving performance

Zonal Statistics
Computing statistics about regions of an image is a core Earth Engine capability. We recently launched a significant performance improvement to batch zonal statistics exports in Earth Engine. We’ve optimized the way we parallelize zonal statistics exports, such as exports that generate statistics for all regions in a large collection. This means that you will get substantially more concurrent compute power per batch task when you use ReduceRegions().

With this launch, large-scale zonal statistics exports are running several times faster than this time last year, meaning you get your results faster, and that Earth Engine can complete even larger analyses than ever. For example, you can now calculate the average tree canopy coverage of every census tract in the continental United States at 1 meter scale in 7 hours. Learn more about how we sped up large-scale zonal statistics computations in our technical blog post.

Python v1
Over the last year, we’ve focused on ease-of-use, reliability, and transparency for Earth Engine Python. The client library has moved into an open-source repository at Google which means we can sync changes to GitHub immediately, keeping you up-to-date on changes between releases. We are also sharing  pre-releases, so you can see and work with Python library candidate releases before they come out. We have a static loaded client library, which makes it easier to build on our Python library and better testing and error messaging. We’ve also continued making progress on improving geemap and integrations like xee.

With all of these changes, we’re excited to announce that the Python Client library is now ‘v1’, representing the maturity of Earth Engine Python. Check out this blog post to read more about these improvements and see how you can take full advantage of Python and integrate it into Google’s Cloud tooling.

COG-backed asset improvements
If you have data stored in Google Cloud Storage (GCS), in Cloud-Optimized GeoTIFF (COG) format, you can easily use it in Earth Engine via Cloud Geotiff Backed Earth Engine Assets, improving the previous experience requiring a single file GeoTIFF, where all bands have the same projection and type.

Now you can create an Earth Engine asset backed by multiple GeoTiffs, which may have different projections, different resolutions, and different band types–and Earth Engine will take care of these complexities for you. There are also major performance improvements to the previous feature: Cloud GeoTiff backed assets now have similar performance to native Earth Engine assets. In addition, If you want to use your GCS COGs elsewhere, like open source pipelines or other tools, the data is stored once and you can use it seamlessly across products.

Looking forward to 2025

We’re excited to see Earth Engine users leverage more advanced tools, stronger security, and seamless integrations to improve sustainability and climate resilience. In the coming year, we’re looking forward to further deepening cloud interoperability, making it easier to develop actionable insights and inform sustainability decision-making through geospatial data.

Conventional fraud detection methods have a hard time keeping up with increasingly sophisticated criminal tactics. Existing systems often rely on the limited data of individual institutions, and this hinders the detection of intricate schemes that span multiple banks and jurisdictions. 

To better combat fraud in cross-border payments, Swift, the global provider of secure financial messaging services, is working with Google Cloud to develop anti-fraud technologies that use advanced AI and federated learning.  

In the first half of 2025, Swift plans to roll out a sandbox with synthetic data to prototype learning from historic fraud, working with 12 global financial institutions, with Google Cloud as a strategic partner. This initiative builds on Swift’s existing Payment Controls Service (PCS), and follows a successful pilot with financial institutions across Europe, North America, Asia and the Middle East.

The partnership: Google Cloud and Swift 

Google Cloud is collaborating with Swift — along with technology partners including Rhino Health and Capgemini — to develop a secure, privacy-preserving solution for financial institutions to combat fraud. This innovative approach uses federated learning techniques, combined with privacy-enhancing technologies (PETs), to enable collaborative intelligence without compromising proprietary data.

Rhino Health will develop and deliver the core federated learning platform, and Capgemini will manage the implementation and integration of the solution.

“Swift is in a unique position in the financial industry – a trusted and cooperative network that is integral to the functioning of the global economy. As such, we are ideally placed to lead collaborative, industry-wide efforts to fight fraud. This exploration will help the community validate whether federated learning technology can help financial institutions stay one step ahead of bad actors through sharing of fraud labels, and in turn enabling them to provide an enhanced cross-border payments experience to their customers,” said Rachel Levi, head of artificial intelligence, Swift. 

“At Google Cloud, we are committed to empowering financial institutions with cutting-edge technology to combat the evolving threat of fraud. Our collaboration with Swift exemplifies the transformative potential of federated learning and confidential computing. By enabling secure collaboration and knowledge sharing without compromising data privacy, we are fostering a safer and more resilient financial ecosystem for everyone,” said Andrea Gallego, Managing Director, global GTM incubation, Google Cloud,  

The challenge: Traditional fraud detection is falling behind

The lack of visibility across the payment lifecycle creates vulnerabilities that can be exploited by criminals. A collaborative approach to fraud modeling offers significant advantages over traditional methods in combating financial crimes. To be effective, this approach requires data sharing across institutions, which is often restricted because of privacy concerns, regulatory requirements, and intellectual property considerations.

The solution: Federated learning

Federated learning offers a powerful solution for collaborative AI model training without compromising privacy and confidentiality. Instead of requiring financial institutions to pool their sensitive data, the model training occurs within financial institutions on decentralized data.

Here’s how it works for Swift: 

1. A copy of Swift’s anomaly detection model is sent to each participating bank. 

2. Each financial institution trains this model locally on their own data.

3. Only the learnings from this training — not the data itself — are transmitted back to a central server for aggregation, managed by Swift. 

4. The central server aggregates these learnings to enhance Swift’s global model. 

This approach significantly minimizes data movement and ensures that sensitive information remains within each financial institution’s secure environment. 

Core benefits of the federated learning solution

By using federated learning solutions, financial institutions can achieve substantial benefits, including:  

• Shared intelligence: Financial institutions work together by sharing information on fraudulent activities, patterns, and trends, which creates a much larger and richer decentralized data pool than any single institution could gather alone.

• Enhanced detection: The collaborative global model can identify complex fraud schemes that might go unnoticed by individual institutions, leading to improved detection and prevention.

• Reduced false positives: Sharing information helps refine fraud models, leading to more accurate identification of genuine threats and fewer false alarms that disrupt both legitimate activity and the customer experience.

• Faster adaptation: The collaborative approach allows for faster adaptation to new fraud trends and criminal tactics. As new threats emerge, the shared knowledge pool helps all participants quickly adjust their models and their fraud prevention tools.

• Network effects: The more institutions participate, the more comprehensive the data pool becomes, creating a powerful network effect that strengthens fraud prevention for everyone involved.

For widespread adoption, federated learning must seamlessly integrate with existing financial systems and infrastructure. This allows financial institutions to easily participate and benefit from the collective intelligence without disrupting their operations.

Architecting the global fraud AI solution

The initial scope remains a synthetic data sandbox centered on prototyping learning from historic payments fraud. The platform allows multiple financial institutions to train a robust fraud detection model while preserving the confidentiality of their sensitive transaction data. It uses federated learning and confidential computing techniques, such as Trusted Execution Environments (TEEs), to enable secure, multi-party machine learning without training data movement.

There are several key components to this solution:

• Federated server in TEE execution environment: A secure, isolated environment where a federated learning (FL) server orchestrates the collaboration of multiple clients by first sending an initial model to the FL clients. The clients perform training on their local datasets, then send the model updates back to the FL server for aggregation to form a global model.

• Federated client: Executes tasks, performs local computation and learning with local dataset (such as  data from an individual financial institution), then submits results back to FL server for secure aggregation.

• Bank-specific encrypted data: Each bank holds its own private, encrypted transaction data that includes historic fraud labels. This data remains encrypted throughout the entire process, including computation, ensuring end-to-end data privacy.

• Global fraud-based model: A pre-trained anomaly detection model from Swift that serves as the starting point for federated learning.

• Secure aggregation: Using a Secure Aggregation protocol to compute these weighted averages would ensure that the server learns only the historic fraud labels from participating financial institutions, but not exactly which  financial institution, thereby preserving the privacy of each participant in the federated learning process.

• Global anomaly detection trained model and aggregated weights: The improved anomaly detection model, along with its learned weights, is securely exchanged back to the participating financial institutions. They can then deploy this enhanced model locally for fraud detection monitoring on their own transactions.

We’re seeing more enterprises adopt federated learning to combat global fraud, including global consulting firm Capgemini. 

“Payment fraud stands as one of the greatest threats that undermines the integrity and stability of the financial ecosystem, with its impact acutely felt upon some of the most vulnerable segments of our society,” said Sudhir Pai, chief technology and innovation officer, Financial Services, Capgemini. 

“This is a global epidemic that demands a collaborative effort to achieve meaningful change. Our application of federated learning is grounded with privacy-by-design principles, leveraging AI to pioneer secure aggregation and anonymization of data which is of primary concern to large financial institutions. The potential to apply our learnings within a singular global trained model across other industries will ensure we break down any siloes and combat fraud at scale,” he said. 

“We are proud to support Swift’s program in partnership with Google Cloud and Capgemini,” said Chris Laws, chief operating officer, Rhino. “Fighting financial crime is an excellent example of the value created from the complex multi-party data collaborations enabled by federated computing, as all parties can have confidence in the security and confidentiality of their data.”

Building a safer financial ecosystem, together

This effort to fight fraud collaboratively will help build a safer and more secure financial ecosystem. By harnessing the power of federated learning and adhering to strong principles of data privacy, security, platform interoperability, confidentiality, and scalability, this solution has the potential to redefine how we combat fraud in the age of fragmented globalized finance and demonstrates a commitment to building a more resilient and trustworthy financial world.

A few months back, we kicked-off Network Performance Decoded, a series of whitepapers sharing best practices for network performance and benchmarking. Today, we’re dropping the second installment – and this one’s about some of those pesky performance limiters you’re bound to run into. In this blog, we’re giving you the inside scoop on how to tackle these issues head-on. 

First up: A Brief Look at Network Performance Limiters

This whitepaper busts the myth that network interface card speed is the be-all and end-all of network performance. Sure, high Mbit/s or Gbit/s sounds great, but packet size and offloading techniques can make a huge difference in how efficiently your network actually runs. Here’s a sneak peek:

• Mbit/s isn’t everything: It’s not just about raw speed. How you package your data (packet size) seriously impacts throughput and CPU usage.

• Bigger packets, better results: Larger packets mean less overhead per packet, which translates to better throughput and less strain on your CPU.

• Offload for a TCP boost: TCP Segmentation Offload (TSO) and Generic Receive Offload (GRO) let your network interface card do some of the heavy lifting, freeing up your CPU and giving your TCP throughput a nice bump — even with smaller packets.

• Watch out for packet-per-second limits: Smaller packets can sometimes hit a bottleneck because of how many packets your system can handle per second.

• At a constant bitrate, bigger packets are more efficient: Even with a steady bitrate, larger packets mean fewer packets overall, which leads to less CPU overhead and a more efficient network.

Get a handle on these concepts and you’ll be able to fine-tune your network for better performance and efficiency, no matter the advertised speed.

Next: A Brief Look at Round Trip Time

This whitepaper dives into TCP Round Trip Time (RTT) — a key network performance metric. You’ll learn how it’s measured, what can throw it off, and how to use that info to troubleshoot network issues like a pro. We’ll show you how the receiving application’s behavior can mess with RTT measurements, and call out some important nuances to consider. For example, TCP RTT measurements do not include the time TCP may spend resending lost segments, which your applications see as latency. Lastly, we’ll show how you can use tools like netperf (also included in our PerfKit Benchmarker toolkit) to get an end-to-end picture.

Finally: A Brief Look at Path MTU Discovery

Last but not least, this whitepaper breaks down Path MTU discovery, a process that helps prevent IP fragmentation. Understanding how networks handle packet sizes can help you optimize your network setup, avoid those frustrating fragmentation issues, and troubleshoot effectively. We’ll even walk you through common problems — like those pesky ICMP blocks leading to large packets being dropped without any notification to the sender — and how to fix them. Plus, you’ll learn the difference between Maximum Transmission Unit (MTU) and Maximum Segment Size (MSS) — knowledge that’ll come in handy when you’re configuring your network and troubleshooting packet size problems.

Stay tuned!

These resources are part of our mission to create an open, collaborative space for network performance benchmarking and troubleshooting. The examples might be from Google Cloud, but the ideas apply everywhere – regardless of where your workloads may be running. You can find all our whitepapers (past, present, and future) on our webpage. Keep an eye out for more!

It’s been more than two and a half years since we introduced AlloyDB for PostgreSQL, our 100% PostgreSQL-compatible database that offers superior performance, availability, and scale. AlloyDB reimagines PostgreSQL with Google’s cutting-edge technology. It includes a scale-out architecture, built-in analytics, and AI/ML-powered management for a truly modern data experience, and is fully managed so you can focus on your application.

PostgreSQL has long been a favorite among developers for its flexibility, reliability, and robust feature set. AlloyDB brings PostgreSQL to the next level with faster performance, stronger functionality, better migration options, and smarter AI capabilities. 

As 2024 comes to a close, it felt like a great time to celebrate with a snazzy AlloyDB overview video and summarize the AlloyDB’s key benefits in an infographic. Whether you’re new to the product or have tried it already, take a look to make sure you’re taking advantage of every benefit. You can also download our in-depth AlloyDB e-book for a deeper dive.

Ready to dive deeper?

Want to learn more about how AlloyDB can revolutionize your PostgreSQL experience? Download our in-depth AlloyDB e-book and discover the transformative ways AlloyDB is redefining what’s possible. You’ll uncover:

• How AlloyDB delivers superior transactional performance at half the cost

• Why AlloyDB is the best database service for building gen AI apps

• The flexibility of running AlloyDB anywhere, on any platform

• How AI-driven development and operations can simplify your database journey

• The power of real-time business insights with AlloyDB’s columnar engine

The future of PostgreSQL is here, and it’s built for you. Start building your next great app with a 30-day AlloyDB free trial.

Think about your favorite apps – the ones that deliver instant results from massive amounts of data. They’re likely powered by vector search, the same technology that fuels generative AI.

Vector search is crucial for developers who need to build applications that are lightning-fast, handle massive datasets, and remain cost-effective, even with huge spikes in traffic. But building and deploying this technology can be a real challenge, especially for gen AI applications that demand incredible flexibility, scale, and speed. In a previous blog post, we showed you how to create production-ready AI applications with features like easy filtering, automatic scaling, and seamless updates. 

Today, we’ll share how Vertex AI’s vector search is tackling these challenges head-on. We’ll explore real-world performance benchmarks demonstrating incredible speed and scalability – all in a cost-effective way.

How does Vertex AI vector search work? 

Imagine you own a popular online store: to keep shoppers happy, your search engine needs to instantly sift through millions of products and deliver relevant results, even during peak shopping seasons. Vector search is a technique for finding similar items within massive datasets. It works by converting data, like text or images, into numerical representations called embeddings. These embeddings capture the semantic meaning of the data, allowing for more accurate and relevant search results. 

For example, imagine your customers are searching for a “navy blue dress shirt.” A keyword search might miss products labeled “midnight blue button-down,” even though they’re essentially the same. Vector search does a better job of surfacing the right products because it uses embeddings to understand the relationships between words and concepts.

You can use it for a wide range of applications, like the e-commerce example shared above, or as a retrieval-augmented generation (RAG) system for generative AI agents, where it grounds responses in your data or recommendation systems that deliver personalized suggestions based on user preferences.

As Xun Wang, Chief Technology Officer of Bloomreach, recently said, “Bloomreach has made the strategic decision to replace OpenAI with Google Vertex AI Embeddings and Vertex AI vector search. Google’s platform delivers clear advantages in performance, scalability, reliability and cost optimization. We’re confident this move will drive significant benefits and we’re thrilled to embark on this new partnership.”

Real-world impact of Vertex AI’s vector search

Our customers are achieving remarkable results with vector search. Here are four standout ways this technology is helping them build high-performance gen AI apps.

#1: The fastest vector search for highly responsive applications

To meet customer expectations, fast response times are critical across search, recommendation systems, and gen AI applications. Studies have consistently found that faster response times directly contribute to an increase in revenue, conversion, and retention.

Vector search is engineered for incredibly low latency at high quality, while maintaining cost-effectiveness.  In our testing, vector search was able to maintain ultra-low latency (9.6ms at P95) and high recall (0.99) while scaling up to 5K queries per second on a dataset of one billion vectors. By achieving such low latencies, Vertex AI vector search ensures that users receive fast, relevant responses, no matter how large the dataset or how many parallel requests hit the system.

As Yuri M. Brovman from eBay wrote in a recent blog post, “[eBay’s vector search] hit a real-time read latency of less than 4ms at 95%, as measured server-side on the Google Cloud dashboard for vector search”.

#2: Massively scalable for all application sizes

Another important consideration in production-ready applications is the ability of your application to support growing data sizes and user bases. 

This means it can easily accommodate sudden spurts in demand, making it massively scalable for applications of any size. Vertex AI vector search can scale up to support billions of embeddings and hundreds of thousands of queries per second while maintaining ultra low latency.

#3: Up to 4X more cost effective

Vertex AI vector search not only maintains performance at scale, but it is also 4x more cost effective than competing solutions, especially for high performance applications. With Vertex AI vector search’s ANN index, you will need significantly less compute for fast and relevant results at scale.

^{Vertex AI vector search’s real-world benchmarks of public datasets by using 2 replicas of n2d machines. Latency was measured at the provided QPS; vector search can scale up beyond this throughput by adding additional replicas.}

#4: It’s highly configurable for all application types

In some scenarios, developers might be interested in trading-off latency for higher recall (or vice versa). For example, an e-commerce website might prioritize speed for quick product suggestions, while a research database might prioritize comprehensive results even if it takes slightly longer. Vector search enables tuning these parameters and hitting higher recall or higher latency, to match business needs. 

Additionally, vector search supports auto-scaling – and when load on the deployment increases, it scales to maintain performance. We measured auto-scaling and found that vector search was able to maintain consistent latency with high recall, as QPS increased from 1K to 5K.

Developers can also increase the number of replicas in order to handle higher throughput, as well as pick different machine types to balance cost and performance. This flexibility makes vector search suitable for a wide range of applications beyond semantic search, including recommendation systems, chatbots, multimodal search, anomaly detection, and image similarity matching.  

Going further with hybrid search

Dense embedding-based semantic search, while excellent at understanding meaning and context, has a weak point: it cannot find items that the embedding model can’t make sense of. Items like product numbers, company’s internal codenames or newly coined terms, aren’t found by semantic search because the embedding model doesn’t understand their meanings. 

With Vertex AI vector search’s hybrid search, building this type of sophisticated search engine is no longer a daunting task. Developers can easily create a single index that incorporates both dense and sparse embeddings, representing semantic meaning and keyword relevance respectively. This streamlined approach allows for rapid development and deployment of high-performance search applications, fully customized to meet specific business needs.

As Nicolas Presta, Sr. Engineering Manager at Mercado Libre wrote, “Most of our successful sales start with a search, so it is important that we give precise results that best match a user’s query. These complex searches are getting better with the addition of the items retrieved from vector search, which will ultimately increase our conversion rate. Hybrid search will unlock more opportunities to uplevel our search engine so that we can create the best customer experience while improving our bottom line.” – Nicolas Presta, Sr. Engineering Manager at Mercado Libre.

In today’s fast-paced digital world, businesses are constantly seeking innovative ways to leverage cutting-edge technologies to gain a competitive edge. AI has emerged as a transformative force, empowering organizations to automate complex processes, gain valuable insights from data, and deliver exceptional customer experiences. 

However, with the rapid adoption of AI comes a significant challenge: managing the associated cloud costs. As AI — and really cloud workloads in general — grow and become increasingly sophisticated, so do their associated costs and potential for overruns if organizations don’t plan their spend carefully.

These unexpected charges can arise from a variety of factors: 

• Human error and mismanagement: Misconfigurations in cloud services (e.g., accidentally enabling a higher-tiered service or changing scaling settings) can inadvertently drive up costs.

• Unexpected workload changes: Spikes in traffic or usage, or changes in application behavior (e.g., marketing campaign or sudden change in user activity) can lead to unforeseen service charges.

• Lack of proactive governance and cost transparency: Without a robust cloud FinOps framework, it’s easy for cloud spending to spiral out of control, leading to significant financial overruns. 

Organizations have an opportunity to proactively manage their cloud costs and avoid budget surprises. By implementing real-time cost monitoring and analysis, they can identify and address potential anomalies before they result in unexpected expenses. This approach empowers businesses to maintain financial control and support their growth objectives.

As one of the world’s leading cybersecurity organizations — serving more than 70,000 organizations in 150 countries — Palo Alto Networks must bring a level of vigilance and awareness to its digital business. Since it experiments often with new technologies and tools and deals with spikes in activity when threat actors mount an attack, the chances for anomalous spending run higher than most.

Recognizing the need of all its customers to effectively manage its cloud spend, Google Cloud launched the Cost Anomaly Detection as part of the Cost Management toolkit. It does not require any setup and automatically detects anomalies for your Google Cloud projects and empowers teams with details to alert and provide root-cause analysis. While Palo Alto Networks used  this feature for a while and found it useful, it eventually realized the need for a customized solution. Due to stringent custom requirements, it wanted a service that could identify anomalies based on labels, such as applications or products that span across Google Cloud projects, and provide more control over anomaly variables that are detected and alerted to its teams. Creating a consistent experience across its multicloud environments was also a priority.

Palo Alto Networks’ purpose-built solution tackles cloud management and AI costs head-on, helping the organization to be proactive at scale. It is designed to enhance cost transparency by providing real-time alerts to product owners, so they can make informed decisions and act quickly. The solution also delivers automated insights at scale, freeing up valuable time for the team to focus on innovation. 

By removing the worry of unexpected costs, Palo Alto Networks can now confidently embrace new cloud and AI workloads, accelerating its digital transformation journey.

Lifecycle of an anomaly 

For Palo Alto Networks, anomalies are unexpected events or patterns that deviate from the norm. In a cloud environment, anomalies can indicate anything from a simple misconfiguration to a full-blown security breach. That’s why it’s critical to have a system in place to detect, analyze, and mitigate anomalies before they can cause significant damage.

This flowchart illustrates the typical lifecycle of an anomaly, broken down into three key stages:

The following sections will take a deeper dive into how Palo Alto Networks used Google Cloud to build its custom AI-powered anomaly solution to address each of these stages.

1. Detection

The first step is to identify potential anomalies.Palo Alto Networks partnered with Google Cloud Consulting to train the ARIMA+ model with billing data from its applications using BigQuery ML (BQML). The team chose this model for its great results for time-series billing data, its ability to customize hyper parameters, and its overall effective cost of operation at scale. 

The ARIMA+ model allowed Palo Alto Networks to generate a baseline spend with upper and lower bounds for its cost anomaly solution.  The team also tuned the model using Palo Alto Networks’ historic billing data, enabling it to inherently understand factors like seasonality, common spikes and dips, migration patterns, and more. If the spend exceeds the upper bound created by the model, the team can then quantify the business cost impact (both percentage and dollar amount) to determine the severity of the alert to be investigated further.

Looker, Google Cloud’s business intelligence platform, serves as the foundation for custom data modeling and visualization, seamlessly integrating with Palo Alto Networks’ existing billing data infrastructure, which continuously streams into BigQuery multiple times a day. This eliminates the need for additional data pipelines, ensuring the team has the most up-to-date information for analysis.

BigQuery MLempowers Palo Alto Networks with robust capabilities for machine learning model training and inference. By leveraging BQML, the team can build and deploy sophisticated models directly within BigQuery, eliminating the complexities of managing separate machine learning environments. This streamlined approach accelerates the ability to detect and analyze cost anomalies in real time. In this case, Palo Alto Networks trained the ARIMA+ model on the last 13 months of billing data for specific applications on the Net Spend field to capture seasonality, spikes and dips, along with migration patterns and known spikes based on a custom calendar. 

To enhance alerting and anomaly management processes, the team also utilizes Google Cloud Pub/Sub and Cloud Run functions. Pub/Sub facilitates the reliable and scalable delivery of anomaly notifications to relevant stakeholders. Cloud Run functions enable custom logic for processing these notifications, including intelligent grouping of similar anomalies to minimize alert fatigue and streamline investigations. This powerful combination allows Palo Alto Networks to respond swiftly and effectively to potential cost issues.

2. Notification and analysis

Once the anomaly is captured, the solution computes the business cost impact and routes alerts to the appropriate application teams through Slack for further investigation. To accelerate root-cause analysis, it synthesizes critical information through text and images to provide all the details about anomaly, pinpointing exactly when it occurred and which SKUs or resources are involved. Application teams can then further analyze this information and, with their application context, quickly arrive at a decision. 

Here is an example of snapshot that captured an increased cost in BigQuery that started on July 30th:

The cost anomaly solution automatically gathered all the information related to the flagged anomalies, such as Google Cloud project ID, data, environment, service names andSKUs, along with the cost impact. This data provided much of the necessary context for the application team to act quickly. Here is an example of the Slack alert:

3. Mitigation

Once the root cause is identified, it’s time to take action to mitigate the anomaly. This may involve anything from making a simple configuration change to deploying a hotfix. In some cases, it may be necessary to escalate the issue and involve cross-functional teams.

In the provided example, a cloud hosted tenant encountered a substantial increase in data volume due to a configuration error. This misconfiguration led to unusually high BigQuery usage. As no default BigQuery reservation existed in the newly established region, the system defaulted to the on-demand pricing model, incurring higher costs. 

To address this, the team procured 100 baseline slots with a 3-year commitment and implemented autoscaling to accommodate any future spikes without impacting performance. To prevent similar incidents, especially in new regions, a long-term cost governance policy was implemented at the organizational level.

Post incident, the cost anomaly solution generates a blameless post mortem document containing the highlights of the actions taken, the impact of collaboration, and the cost savings achieved through timely detection and mitigation. This document focuses on: 

• A detailed timeline of events: This list might include when a cost increase was captured, when the team was alerted, and the mitigation plan with short-term and long-term initiatives to prevent this in future.

• Actions taken: This description includes details about anomaly detection, the analysis conducted by the application team, and mitigative actions taken. 

• Preventative strategy: This describes the short-term and long-term plan to avoid similar future incidents.

• Cost impact and cost avoidance: These calculations include the overall cost incurred from the anomaly and estimate the additional cost if the issue had not been detected in a timely manner.

A formal communication is then sent out to the Palo Alto Networks application team, including leadership, for further visibility. 

From its experience working at scale, Palo Alto Networks has learned to embrace the fact that anomalies are unavoidable in cloud environments. To manage them effectively, a well-defined lifecycle encompassing detection, analysis, and mitigation is crucial. Automated monitoring tools play a key role in identifying potential anomalies, while collaboration across teams is also essential for successful resolution. In particular, the team places huge emphasis on the importance of continuous improvement for optimizing the anomaly management process. For example, they established the reporting dashboard below for long-term continuous governance.

By leveraging the power of AI and partnering with Google Cloud, Palo Alto Networks is enabling businesses to unlock the full potential of AI while ensuring responsible and sustainable cloud spending. With a proactive approach to cost anomaly management, organizations can confidently navigate the evolving landscape of AI, drive innovation, and achieve their strategic goals. Check out the public preview of Cost Anomaly Detection or reach out to Google Cloud Consulting for a customized solution. 

_{We are extremely grateful to the entire team for partnering together to build this solution: Yaping Gu, Matt Orr, Andy Crutchfield, and Gina Huh.}

Mapping the user experience is one of the most persistent challenges a business can face. Fullstory, a leading behavioral data analytics platform, helps organizations identify pain points and optimize digital experiences by reproducing  user sessions and sharing strong analytics highlighting areas for improvement in the customer’s journey. This boosts conversion rates, reduces churn, and enhances customer satisfaction. 

AI has made this even stronger. Fullstory’s comprehensive AI-powered autocapture technology, Fullcapture, removes the need for manual instrumentation and uncovers hidden patterns that might otherwise be missed. 

Today, we’ll share how Fullstory leverages Vertex AI serving Gemini 1.5 Pro to strengthen their autocapture technology.

How Vertex AI and AI agents help Fullstory measure the user experience

Think of Fullcapture as a video recorder for your website or app, capturing every interaction in detail. Traditional autocapture methods are more like transcription services, logging only selected highlights and often missing the complete picture. With Fullcapture, no user action goes unrecorded, with minimal impact on device performance. Operating server-side, Fullcapture allows for revisiting any aspect of user behavior as needed. If a new signal is required, it can be easily retrieved from the recorded data without affecting client-side performance.

The table below breaks down how Fullcapture goes beyond traditional autocapture capabilities to give users a deeper understanding of their customer data.

By integrating its Fullcapture capabilities with Google’s Vertex AI serving Gemini 1.5 Pro, Fullstory empowers customers to effortlessly analyze this extensive data and focus on what truly matters. Driven by a proactive AI agent, Fullstory enables faster data discovery by highlighting important elements and automatically categorizing user interactions into semantic components, providing even deeper levels of analysis.

AI-powered data discovery 

Data discovery is a 6-step process that involves exploring, classifying, and analyzing data from various sources to uncover patterns and extract actionable insights. This process allows users to visually navigate data relationships and apply advanced analytics to optimize business decisions and performance.

To effectively analyze user behavior, businesses need to identify and label key elements on their websites (e.g., buttons, forms). This process can be tedious and time-consuming. Fullstory’s AI agent, powered by Gemini 1.5 Pro, automates this critical task by scraping data from user interactions and making intelligent decisions at various stages—identifying key elements, determining their significance, and autonomously categorizing them. This multi-stage decision-making process not only streamlines workflows but also ensures businesses can focus on deriving actionable insights rather than manual labeling.

Within Fullstory, “elements” allow users to label UI components based on specific CSS selectors. A CSS selector is a pattern used to target elements in a webpage, such as classes, IDs, or attributes. For instance, a “Checkout Button” element might be created with the selector .checkout-page-container [data-testid="primary-button"]. These labels help categorize UI components and utilize them for product analytics. Broad semantic labeling is crucial for long-term success with Fullstory, and automating this process simplifies workflows for users.

Vertex AI with Gemini 1.5 Pro offers a unique opportunity to add a human touch at scale. It proactively identifies and describes web components, ultimately providing actionable insights that benefit Fullstory customers. Gemini 1.5 Pro is trained on extensive web expertise, including web implementation from CSS and web frameworks like React, along with a vast dataset of website images.

For example, the model can analyze a website screenshot and accurately describe its components, understanding both the overall structure, visible text, and the logical structure of the web page. This understanding can be further enhanced with web implementation details, such as CSS selectors, to gain a deeper understanding of specific components.

Optimizing for accurate element identification

Fullstory employs a meticulous approach to ensure the model provides high-quality element suggestions in four critical ways:

1. Strategic prompt engineering: Complex tasks are broken down into smaller, manageable steps, allowing the model to build a foundational understanding and deliver consistent and accurate results.

2. Pre-filtering with heuristics: Heuristics pre-filter potential elements before requests are sent to Vertex AI with Gemini, optimizing efficiency.

3. Validation with Vertex AI with Gemini: The model’s expertise validates potential elements, ensuring that only useful suggestions are presented.

4. Contextualized suggestions: Each suggestion includes a screenshot, CSS selector, and occurrence metrics, providing valuable context for informed decision-making.

This process ensures effective and efficient use of Gemini’s AI capabilities, resulting in accurate and valuable element suggestions.

Pinpointing and perfecting: How we identify and label key web elements

Optimizing the digital experience requires identifying and understanding key web elements. These elements need to be defined in a way that remains resilient to website changes. This presents a challenge, given the diverse nature of websites and user behaviors.

In the real world, an element selector can look something like:

While metrics like “most clicked buttons” provide some insight, a more sophisticated approach is needed to uncover elements that drive engagement, signal errors, or reveal hidden opportunities. Effective management of potentially long and brittle element selector definitions is also crucial for maintaining data quality.

The search for meaningful elements

Fullstory captures every user interaction, generating a wealth of data. The platform continuously monitors unrecognized components, prioritizing:

• New feature discovery: Identifying elements on newly launched feature pages.

• Power user behavior: Understanding how experienced users interact with the website.

• Error signals: Detecting elements with CSS that suggests potential errors.

• Content analysis: Analyzing elements containing text that indicates user intent or potential issues.

These searches utilize CSS selectors to precisely target elements for granular analysis and efficient refinement.

Analyzing user behavior in Fullstory often involves crafting complex CSS selectors. With Vertex AI and Gemini 1.5 Pro, this process is simplified.

• Deep indexing: Components of website CSS selectors and associated events are tokenized and indexed, enabling efficient searching through countless variations.

• Semantic relevance: The model understands the meaning behind selectors. For example, when tracking an “Add to Cart” button, the model recognizes that the class .add-to-cart is more relevant than a generic class like .primary-button.

• Powerful search: Combining semantic understanding with advanced search capabilities, the model identifies the best match for selectors.

This results in high-quality selectors without requiring in-depth CSS expertise, allowing users to focus on uncovering valuable insights from their Fullstory data.

Here’s an example of a Fullstory element being optimized:

The Importance of accurate labeling

Once an element is identified through its CSS selector, accurate labeling becomes crucial. This involves:

• Name: A clear and concise name reflecting the element’s function.

• Description: A detailed explanation of the element’s purpose and behavior.

• Role: Assigning a predefined role from Fullstory’s library (e.g., “Add to Cart Button,” “Validation Error”).

Adding context to the equation

To ensure high-quality and consistent element labeling, Vertex AI with Gemini 1.5 Pro leverages Fullstory’s rich data and advanced AI capabilities to provide comprehensive context:

• Visual representation: Screenshots of the element in action, generated from session playbacks.

• Textual analysis: Examining text occurrences associated with the element.

• Location tracking: Identifying the URLs where the element appears.

This approach, combining Fullstory’s data capture with Vertex AI and Gemini 1.5 Pro, allows for AI-powered analysis that moves beyond basic metrics to truly understand user behavior. By identifying and labeling key web elements with precision and context, businesses can unlock valuable insights and create exceptional digital experiences.

Delivering real-world results with Vertex AI API for Gemini 1.5 Pro

The collaboration between Fullstory and Google Cloud has yielded tens of thousands of element suggestions generated for customers, with Gemini 1.5 Pro intelligently filtering out a significant portion of irrelevant suggestions. The model has also identified numerous error elements that were previously unrecognized.

Beyond element identification, the mapping between element configuration and screenshots has opened up new opportunities for improving site configuration and enhancing analytics. This ongoing collaboration between Fullstory and Google Cloud continues to drive significant value for customers, empowering them to gain a deeper understanding of user behavior and optimize their digital experiences.

Ready to unlock the power of behavioral data? Visit Fullstory on Google Cloud Marketplace today! With Fullstory, you can gain a deeper understanding of your customers by uncovering hidden insights into their behavior and identifying key opportunities to optimize their digital experience.

Want to learn more about leveraging AI to enhance your Fullstory experience? Explore Fullstory’s documentation or try out this collaboration to see how AI can accelerate your journey.

From helping your developers write better code faster with Code Assist, to helping cloud operators more efficiently manage usage with Cloud Assist, Gemini for Google Cloud is your personal AI-powered assistant. 

However, understanding exactly how your internal users are using Gemini has been a challenge — until today. 

Today we are announcing that Cloud Logging and Cloud Monitoring support for Gemini for Google Cloud. Currently in public preview, Cloud Logging records requests and responses between Gemini for Google Cloud and individual users, while Cloud Monitoring reports 1-day, 7-day, and 28-day Gemini for Google Cloud active users and response counts in aggregate.

Cloud Logging

In addition to offering customers general visibility into the impact of Gemini, there are a few scenarios where logs are useful:

• to track the provenance of your AI-generated content

• to record and review user usage of Gemini for Google Cloud 

This feature is available as opt-in and when enabled, logs your users’ Gemini for Google Cloud activity to Cloud Logging (Cloud Logging charges apply). 

Once enabled, log entries are made for each request to and response from Gemini for Google Cloud. In a typical request entry, Logs Explorer would provide an entry similar to the following example:

There are several things to note about this entry:

• The content inside jsonPayload contains information about the request. In this case, it was a request to complete Python code with def fibonacci as the input. 

• The labels tell you the method (CompleteCode), the product (code_assist), and the user who initiated the request (cal@google.com). 

• The resource labels tell you the instance, location, and resource container (typically project) where the request occurred. 

In a typical response entry, you’ll see the following:

Note that the request_id inside the label are identical for this pair of requests and responses, enabling identification of request and response pairs.

In addition to the Log Explorer, Log Analytics supports queries to analyze your log data, and help you answer questions like “How many requests did User XYZ make to Code Assist?” 

For more details, please see the Gemini for Google Cloud logging documentation. 

Cloud Monitoring 

Gemini for Google Cloud monitoring metrics help you answer questions like: 

• How many unique active users used Gemini for Google Cloud services over the past day or seven days? 

• How many total responses did my users receive from Gemini for Google Cloud services over the past six hours?

Cloud Monitoring support for Gemini for Google Cloud is available to anybody who uses a Gemini for Google Cloud product and records responses and active users as Cloud Monitoring metrics, with which dashboards and alerts can be configured. 

Because these metrics are available with Cloud Monitoring, you can also use them as part of Cloud Monitoring dashboards. A “Gemini for Google Cloud” dashboard is automatically installed under “GCP Dashboards” when Gemini for Google Cloud usage is detected:

Metrics Explorer offers another avenue where metrics can be examined and filters applied to gain a more detailed view of your usage. This is done by selecting the “Cloud AI Companion Instance” active resource in the Metrics Explorer:

In the example above, response_count is the number of responses sent by Gemini for Google Cloud, and can be filtered for Gemini Code Assist or the Gemini for Google Cloud method (code completion/generation). 

For more details, please see the Gemini for Google Cloud monitoring documentation.

What’s next

We’re continually working on additions to these new capabilities, and in particular are focused on Code Assist logging and metrics enhancements that will bring even further insight and observability into your use of Gemini Code Assist and its impact. To get started with Gemini Code Assist and learn more about Gemini Cloud Assist — as well as observability data about it from Cloud Logging and Monitoring — check out the following links: 

• Gemini Code Assist overview

• Gemini Cloud Assist product page

• View Gemini for Google Cloud logs

• Monitor Gemini for Google Cloud usage

The automotive industry is facing a profound transformation, driven by the rise of CASE, — connected cars, autonomous and automated driving, shared mobility, and electrification. Simultaneously,  manufacturers face the imperative to further increase efficiency, automate manufacturing, and improve quality.  AI has emerged as a critical enabler of this evolution. In this dynamic landscape, Toyota turned to Google Cloud’s AI Infrastructure to build an innovative AI Platform that empowers factory workers to develop and deploy machine learning models across key use cases.

Toyota‘s renowned production system, Toyota Production System, rooted in the principles of “Jidoka” (automation with a human touch) and “Just-in-Time” inventory management, has long been the gold standard in manufacturing efficiency. However, certain parts of this system are resistant to conventional automation. 

We started experimenting with using AI internally in 2018. However, a shortage of employees with the expertise required for AI development created a bottleneck in promoting its wider use. Seeking to overcome these limitations, Toyota’s Production Digital Transformation Office embarked on a mission  to democratize AI development within its factories in 2022. 

Our goal was to build an AI Platform that enabled factory floor employees, regardless of their AI expertise, to create machine learning models with ease. This would facilitate the automation of manual, labor-intensive tasks, freeing up human workers to focus on higher-value activities such as process optimization, AI implementation in other production areas, and data-driven decision-making.

AI Platform is the collective term for the AI technologies we have developed, including web applications that enable easy creation of learning models on the manufacturing floor, compatible equipment on the manufacturing line, and the systems that support these technologies.

By the time we completed implementing the AI platform earlier this year, we found it would be able to save us as many as 10,000 hours of mundane work annually through manufacturing efficiency and process optimization.

For this company-wide initiative, we brought the development in-house to accumulate know-how. It was also important to stay up-to-date with the latest technologies so we could accelerate development and broaden opportunities to deploy AI. Finally, it was crucial to democratize our AI technology into a truly easy-to-use platform. We knew we needed to be  led by those working on the manufacturing floor if we wanted them to use the AI more proactively; while at the same time, we wanted to improve the development experience for our software engineers.

Hybrid Architecture Brings Us Numerous Advantages

To power our AI Platform, we opted for a hybrid architecture that combines our on-premises infrastructure and cloud computing.

The first objective was to promote agile development. The hybrid cloud environment, coupled with a microservices-based architecture and agile development methodologies, allowed us to rapidly iterate and deploy new features while maintaining robust security. The path for a microservices architecture arose from the need to flexibly respond to changes in services and libraries, and as part of this shift, our team also adopted a development method called “SCRUM” where we release features incrementally in short cycles of a few weeks, ultimately resulting in streamlined workflows.

If we had developed machine learning systems solely on-premises with the aim to ensure security, we would need to perform security checks on a large amount of middleware, including dependencies, whenever we add a new feature or library. On the other hand, with the hybrid cloud, we can quickly build complex, high-volume container images while maintaining a high level of security.

The second objective is to use resources effectively. The manufacturing floor, where AI models are created, is now also facing strict cost efficiency requirements. 

With a hybrid cloud approach, we can use on-premises resources during normal operations and scale to the cloud during peak demand, thus reducing GPU usage costs and optimizing performance. This allows us to flexibly adapt to an expected increase in the number of users of AI Platform in the future, as well.

Furthermore, adapting a hybrid cloud helps us to achieve cost savings on facility investments. By leveraging the cloud for scaling capacity, we minimized the need for extensive on-premises hardware investments. In a traditional on-premises environment, we would need to set up high-performance servers with GPUs in every factory. With a hybrid cloud, we can reduce the number of on-premises servers to one and use the cloud to cover the additional processing capacity whenever needed. The hybrid cloud’s concept of  “using resources when and only as needed” aligns well with our “Just-in-Time” method. 

The Reasons We Chose Google Cloud AI Hypercomputer 

Several factors influenced our decision when choosing a cloud partner for the development of the Toyota AI Platform’s hybrid architecture and ultimately, we chose Google Cloud. 

The first is the flexibility of using GPUs. In addition to the availability of using high-performance GPUs from one unit, we could use A2 VMs with Google Cloud’s unique features like multi-instance GPUs and time-sharing GPUs. This flexibility reduces  idle compute resources and optimizes costs, leading to increased business value over a given time by allowing scarce GPUs to perform more machine learning trainings. Plus Dynamic Workload Scheduler helps us efficiently manage and schedule GPU resources to help us optimize running costs. 

Next is ease of use. We anticipate that we will be required to secure more GPU resources across multiple regions in the future. With Google Cloud, we can manage GPU resources through a single VPC, avoiding network complexity. When considering the system to deploy, only Google Cloud had this capability.

The speed of build and processing was also a big appeal for us . In particular, Google Kubernetes Engine (GKE), its Autopilot and Image Streaming provide flexibility and speed, thereby allowing us to  improve cost-effectiveness in terms of operational burden. We measured the communication speed of containerization during the system evaluation process, and found that Google Cloud was four times faster scaling from zero than other existing services. The speed of communication and processing is extremely important, as we use up to 10,000 images when creating the learning model. When we first started developing AI technology in-house, we struggled with flexible system scaling and operations. In this regard, too, using Google Cloud was the ideal choice.

Completed Large-scale Development in 1.5 Years with 6 Members

With Google Cloud’s support, a small team of six developers achieved a remarkable feat by successfully building and deploying the AI Platform in about half the time it would take for a standard system development project at Toyota. This rapid development was facilitated by Google Cloud’s user-friendly tools, collaborative approach, and alignment with Toyota’s automation-focused culture.

After choosing Google Cloud, we began discussing the architecture with the Google Cloud team. We then worked on modifying the web app architecture for the cloud lift, building the hybrid cloud, developing human resources within the company, while learning skills for the “internalization of technology (acquisition and accumulation of new know-how)”.During the implementation process, we divided the  workloads into on-premises and cloud architectures, and implemented best practices to monitor communications and resources. This process also involved migrating CI/CD pipelines and image data to the cloud. By performing builds in the cloud and caching images on-premises, we ensured quick start-up and flexible operations.

In addition to the ease of development of Google Cloud products, cultural factors also contributed greatly to the success of this project. Our objective of making the manufacturing process automated as much as possible, is in line with Google’s concept of SRE (Site Reliability Engineering). So, we shared the same sense of purpose.

Currently, in the hybrid cloud, we deploy a GKE Enterprise cluster on-premises and link it to the GKE cluster on Google Cloud. When we develop our AI Platform and web apps, we run Cloud Build with Git CI triggers, verify container image vulnerabilities with Artifact Registry and Container Analysis, and ensure a secure environment with Binary Authorization. At the manufacturing floor, structural data such as numerical data and unstructured data such as images are deployed on GKE via a web app, and learning models are created on N1 VMs with NVIDIA T4 GPUs and  A2 VMs which include NVIDIA A100 GPUs.

Remarkable Results Achieved Through Operating AI Platform

We have achieved remarkable results with this operational structure. 

Enhanced Developer Experience: First, with regard  to the development experience, waiting time for tasks have been reduced, and operational and security burdens have been lifted, allowing us to focus even more on development.

Increased User Adoption: Additionally, the use of our AI Platform on the manufacturing floor is also growing. Creating a learning model can typically take 10 to 15 minutes in the shortest, and up to 10 hours in the longest. GKE’s Image Streaming streamlined pod initialization and accelerated learning, resulting in a 20% reduction in learning model creation time. This improvement has enhanced the user experience (UX) on the manufacturing floor, leading to a surge in the number of users. Consequently, the number of models created in manufacturing has steadily increased, rising from 8,000 in 2023 to 10,000 in 2024. The widespread adoption of this technology has allowed for a substantial reduction of over 10,000 man-hours per year in the actual manufacturing process, optimizing efficiency, and productivity. 

Expanding Impact: AI Platform is already in use at all of our car and unit manufacturing factories (total 10 factories), and its range of applications is expanding. At the Takaoka factory, the platform is used not only to inspect finished parts, but also in the manufacturing process; inspect the application of adhesives used to attach glass to back doors, and to detect abnormalities in injection molding machines used for manufacturing bumpers and other parts. Meanwhile, the number of active users in the company has increased to nearly 1,200, and more than 400 employees participate in in-house training programs each year.

Recently, there have been cases where people who were developing in other departments became interested in Google Cloud and joined our development team. Furthermore, this project has sparked an unprecedented shift within the company: the resistance to the cloud technology itself diminishing and other departments beginning to consider adopting it.

Utilizing Cloud Workstations for Further Productivity With an Eye on Generative AI

For  the AI Platform, we plan to develop an AI model that can set more detailed criteria for detection, implement it in an automated picking process, and use it for maintenance and predictive management of the entire production line. We are also developing original infrastructure models based on the big data collected on the platform, and expect to use the AI Platform more proactively in the future.Currently, the development team compiles work logs and feedback from the manufacturing floor, and we believe that the day will soon come when we will start utilizing generative AI. For example, the team is considering using AI to create images for testing machine learning during the production preparation stage, which has been challenging due to a lack of data. In addition, we are considering using Gemini Code Assist to improve the developer experience, or using Gemini to convert past knowledge into RAG and implement a recommendation feature.In March 2024, we joined Google Cloud’s Tech Acceleration Program (TAP) and implemented Cloud Workstations. This also aims to achieve the goals we have been pursuing: to improve efficiency, reduce workload, and create a more comfortable work environment by using managed services.

Through this project, led by the manufacturing floor, we have established a “new way of manufacturing” where anyone can easily create and utilize AI learning models, and significantly increase the business impact for our company. This was enabled by the cutting-edge technology and services provided by Google Cloud.

Like “Jidoka (auto’no’mation)” of production lines and “Just-in-Time” method, the AI Platform has now become an indispensable part of our manufacturing operations. Leveraging Google Cloud, we will continue our efforts to make ever-better cars.