Tibor Kiss

Today, we’re expanding the choice of third-party models available in Vertex AI Model Garden with the addition of Anthropic’s newest generation of the Claude model family: Claude Opus 4 and Claude Sonnet 4. Both Claude Opus 4 and Claude Sonnet 4 are hybrid reasoning models, meaning they offer modes for near-instant responses and extended thinking for deeper reasoning.  

Claude Opus 4 is Anthropic’s most powerful model to date. Claude Opus 4 excels at coding, with sustained performance on complex, long-running tasks and agent workflows. Use cases include advanced coding work, autonomous AI agents, agentic search and research, tasks that require complex problem solving, and long-running tasks that require precise content management. 

Claude Sonnet 4 is Anthropic’s mid-size model that balances performance with cost. It surpasses its predecessor, Claude Sonnet 3.7, across coding and reasoning while responding more precisely to steering. Use cases include coding tasks such as code reviews and bug fixes, AI assistants, efficient research, and large-scale content generation and analysis.

Claude Opus 4 and Claude Sonnet 4 are generally available as a Model-as-a-Service (MaaS) offering on Vertex AI. For more information on the newest Claude models, visit Anthropic’s blog.

Build advanced agents on Vertex AI

Vertex AI is Google Cloud’s comprehensive platform for orchestrating your production AI workflows across three pillars: data, models, and agents—a combination that would otherwise require multiple fragmented solutions. A key component of the model pillar is Vertex AI Model Garden, which offers a curated selection of over 200 foundation models, including Google’s models, third-party models, and open models—empowering you to choose the ideal solution for your specific needs.

You can leverage Vertex AI’s Model-as-a-Service (MaaS) to rapidly deploy and scale Claude-powered intelligent agents and applications, benefiting from integrated agentic tooling, fully managed infrastructure, and enterprise-grade security.

By building on Vertex AI, you can: 

• Orchestrate sophisticated multi-agent systems: Build agents with an open approach using Google’s Agent Development Kit (ADK) or your preferred framework. Deploy your agents to production with enterprise-grade controls directly in Agent Engine. 
• Harness the power of Google Cloud integrations: You can connect Claude directly within BigQuery ML to facilitate functions like text generation, summarization, translation, and more.
• Optimize performance with provisioned throughput: Reserve dedicated capacity and prioritized processing for critical production workloads with Claude models at a fixed fee. To get started with provisioned throughput, contact your Google Cloud sales representative.
• Maximize Claude model utilization: Reduce latency and costs while increasing throughput by employing Vertex AI’s advanced features for Claude models such as batch predictions, prompt caching, token counting, and citations. For detailed information, refer to our documentation.
• Scale with fully managed infrastructure: Vertex AI’s fully managed and AI-optimized infrastructure simplifies how you deploy your AI workloads in production. Additionally, Vertex AI’s new global endpoints for Claude (public preview) enhance availability by dynamically serving traffic from the nearest available region.
• Build confidently with enterprise-grade security and compliance: Benefit from Vertex AI’s built-in security and compliance measures that satisfy stringent enterprise requirements.

Customers achieving real impact with Claude on Vertex AI

To date, more than 4,000 customers have started using Anthropic’s Claude models on Vertex AI. Here’s a look at how top organizations are driving impactful results with this powerful integration:

Augment Code is running its AI coding assistant, which specializes in helping developers navigate and contribute to production-grade codebases, with Anthropic’s Claude models on Vertex AI.

“What we’re able to get out of Anthropic is truly extraordinary, but all of the work we’ve done to deliver knowledge of customer code, used in conjunction with Anthropic and the other models we host on Google Cloud, is what makes our product so powerful.” – Scott Dietzen, CEO, Augment Code 

Palo Alto Networks is accelerating software development and security by deploying Claude on Vertex AI.

“With Claude running on Vertex AI, we saw a 20% to 30% increase in code development velocity. Running Claude on Google Cloud’s Vertex AI not only accelerates development projects, it enables us to hardwire security into code before it ships.” – Gunjan Patel, Director of Engineering, Office of the CPO, Palo Alto Networks

Replit leverages Claude on Vertex AI to power Replit Agent, which empowers people across the world to use natural language prompts to turn their ideas into applications, regardless of coding experience.

“Our AI agent is made more powerful through Anthropic’s Claude models running on Vertex AI. This integration allows us to easily connect with other Google Cloud services, like Cloud Run, to work together behind the scenes to help customers turn their ideas into apps.” – Amjad Masad, Founder and CEO, Replit

Get started 

To get started with the new Claude models on Vertex AI, navigate to the Claude Opus 4 or the Claude Sonnet 4 model card in Vertex AI Model Garden, select “Enable”, and follow the proceeding instructions. 

You can also find and easily procure Claude Opus 4 and Claude Sonnet 4 on Google Cloud Marketplace.

Explore our sample notebook and documentation to start building.

In today’s data-driven world, understanding large datasets often requires numerous, complex non-additive¹ aggregation operations. But as the size of the data becomes massive², these types of operations become computationally expensive and time-consuming using traditional methods. That’s where Apache DataSketches come in. We’re excited to announce the availability of Apache DataSketches functions within BigQuery, providing powerful tools for approximate analytics at scale.

Apache DataSketches is an open-source library of sketches, specialized streaming algorithms that efficiently summarize large datasets. Sketches are small probabilistic data structures that enable accurate estimates of distinct counts, quantiles, histograms, and other statistical measures – all with minimal memory, minimal computational overhead, and with a single pass through the data. All but a few of these sketches provide mathematically proven error bounds, i.e., the maximum possible difference between a true value and its estimated or approximated value. These error bounds can be adjusted by the user as a trade-off between the size of the sketch and the size of the error bounds. The larger the configured sketch, the smaller will be the size of the error bounds. 

With sketches, you can quickly gain insights from massive datasets, especially when exact computations are impractical or impossible. The sketches themselves can be merged, making them additive and highly parallelizable, so you can combine sketches from multiple datasets for further analysis. This combination of small size and mergeability can translate into orders-of-magnitude improvement in speed of computational workload compared to traditional methods.

Why DataSketches in BigQuery?

BigQuery is known for its ability to process petabytes of data, and DataSketches are a natural fit for this environment. With DataSketches functions, BigQuery lets you:

• Perform rapid approximate queries: Get near-instantaneous results for distinct counts, quantile analysis, adaptive histograms and other non-additive aggregate calculations on massive datasets.

• Save on resources: Reduce query costs and storage requirements by working with compact sketches instead of raw data.

• Move between systems: DataSketches have well-defined stored binary representations that let sketches be transported between systems and interpreted by three major languages: Java, C++, and Python, all without losing any accuracy.

Apache DataSketches come to BigQuery through custom C++ implementations using the Apache DataSketches C++ core library compiled to WebAssembly (WASM) libraries, and then loaded within BigQuery Javascript user-defined aggregate functions (JS UDAFs). 

How BigQuery customers use Apache DataSketches

Yahoo started the Apache DataSketches project in 2011, open-sourced it in 2015, and still uses the Apache DataSketches library. They use approximate results in various analytic query operations such as count distinct, quantiles, and most frequent items (a.k.a. Heavy Hitters). More recently, Yahoo adapted the DataSketches library to leverage the large scale of BigQuery, using the Google-defined JavaScript User Defined Aggregate Functions (UDAF) interface to the Google Cloud and BigQuery platform.

“Yahoo has successfully used the Apache DataSketches library to analyze massive data in our internal production processing systems for more than 10 years. Data sketching has allowed us to respond to a wide range of queries summarizing data in seconds, at a fraction of the time and cost of brute-force computation. As an early innovator in developing this powerful technology, we are excited about this fast, accurate, large-scale, open-source technology becoming available to those already working in a Google Cloud BigQuery environment.” – Matthew Sajban, Director of Software Development Engineering, Yahoo

Featured sketches

So, what can you do with Apache DataSketches? Let’s take a look at the sketches integrated with BigQuery.

Cardinality sketches

• Hyper Log Log Sketch (HLL): The DataSketches library implements this historically famous sketch algorithm with lots of versatility. It is best suited for straightforward distinct counting (or cardinality) estimation. It can be adapted to a range of sizes from roughly 50 bytes to about 2MB depending on the accuracy requirements. It also comes in three flavors: HLL_4, HLL_6, HLL_8 that enable additional tuning of speed and size.

• Theta Sketch: This sketch specializes in set expressions and allows not only normal additive unions but also full set expressions between sketches with set-intersection and set-difference. Because of its algebraic capability, this sketch is one of the most popular sketches. It has a range of sizes from a few hundred bytes to many megabytes, depending on the accuracy requirements.

• CPC Sketch: This cardinality sketch takes advantage of recent algorithmic research and enables smaller stored size, for the same accuracy, than the classic HLL sketch. It is targeted for situations where accuracy per stored size is the most critical metric.

• Tuple Sketch: This extends Theta Sketch to enable the association of other values with each unique item retained by the sketch. This allows the computation of summaries of attributes like impressions or clicks as well as more complex analysis of customer engagement, etc.

Quantile sketches

• KLL Sketch: This Sketch is designed for quantile estimation (e.g., median, percentiles), and ideal for understanding distributions, creating density and histogram plots, and partitioning large data sets. The KLL algorithm used in this sketch has been proven to have statistically optimal quantile approximation accuracy for a given size. The KLL Sketch can be used with any kind of data that is comparable, i.e., has a defined sorting order between items. The accuracy of KLL is insensitive to the input data distribution.

• REQ Sketch: This quantile sketch is designed for situations where accuracy at the ends of the rank domain is more important than at the median. In other words, if you’re most interested in accuracy at the 99.99th percentile and not so interested in the accuracy at the 50th percentile, this is the sketch to choose. Like the KLL Sketch, this sketch has mathematically proven error bounds. The REQ sketch can be used with any kind of data that is comparable, i.e., has a defined sorting order between items. By design, the accuracy of REQ is sensitive to how close an item is to the ends of the normalized rank domain (i.e., close to rank 0.0 or rank 1.0), otherwise it is insensitive to the input distribution.

• T-Digest Sketch: This is also a quantile sketch, but it’s based on a heuristic algorithm and doesn’t have mathematically proven error properties. It is also limited to strictly numeric data. The accuracy of the T-Digest Sketch can be sensitive to the input data distribution. However, it’s a very good heuristic sketch, fast, has a small footprint, and can provide excellent results in most situations.

Frequency sketches

• Frequent Items Sketch: This sketch is also known as a Heavy-Hitter sketch. Given a stream of items, this sketch identifies, in a single pass, the items that occur more frequently than a noise threshold, which is user-configured by the size of the sketch. This is especially useful in real-time situations. For example, what are the most popular items from a web site that are being actively queried, over the past hour, day, or minute? Its output is effectively an ordered list of the most frequently visited items. This list changes dynamically, which means you can query the sketch, say, every hour to help you understand the query dynamics over the course of a day. In static situations, for example, it can be used to discover the largest files in your database in a single pass and with only a modest amount of memory.

How to get started

To leverage the power of DataSketches in BigQuery, you can find the new functions within the bqutil.datasketches dataset (for US multi-region location) or bqutil.datasketches_<bq_region> dataset (for any other regions and locations). For detailed information on available functions and their usage, refer to the DataSketches README. You can also find demo notebooks in our GitHub repo for the KLL Sketch, Theta Sketch, and FI Sketch. 

Example: Obtaining estimates of Min, Max, Median, 75th, 95th percentiles and total count using the KLL Quantile Sketch

Suppose you have 1 million comparable³ records in 100 different partitions or groups. You would like to understand how the records are distributed by their percentile or rank, without having to bring them all together in memory or even sort them.   

SQL:

This code produces the result:

Example: Estimating user engagement metrics

The DataSketches Tuple Sketch is a powerful tool to analyze properties that have a natural association with unique identifiers.

For example, imagine you have a large-scale web application that records user identifiers and their clicks on various elements. You would like to analyze this massive dataset efficiently to obtain approximate metrics for clicks per unique user. The Tuple Sketch computes the number of unique users and allows you to track additional properties that are naturally associated with the unique identifiers as well.

SQL:

This produces the following result:

Get faster, more accurate estimations

In short, DataSketches in BigQuery unlocks a new dimension of approximate analytics, helping you gain valuable insights from massive datasets quickly and efficiently. Whether you’re tracking website traffic, analyzing user behavior, or performing any other large-scale data analysis, DataSketches are your go-to tools for fast, accurate estimations.

To start using DataSketches in BigQuery, refer to the DataSketches-BigQuery repository README for building, installing and testing the DataSketches-BigQuery library in your own environment. In each sketch folder there is a README that details the specific function specifications available for that sketch.

If you are working in a BigQuery environment, the DataSketches-BigQuery library is already available for you to use in all regional public BigQuery datasets.

<sup>1. Examples include distinct counting, quantiles, topN, K-means, density estimation, graph analysis, etc.
The results from one parallel partition cannot be simply “added” to the results of another partition – thus the term non-additive (a.k.a. non-linear operations).
2. Massive ~ typically, much larger than what can be conveniently kept in random-access memory.
3. Any two items can be compared to establish their order, i.e. if A < B, then A precedes B.</sup>

Want to save some money on large AI training? For a typical PyTorch LLM training workload that spans thousands of accelerators for several weeks, a 1% improvement in ML Goodput can translate to more than a million dollars in cost savings¹. Therefore, improving ML Goodput is an important goal for model training — both from an efficiency perspective, as well as for model iteration velocity. 

However, there are several challenges to improving ML Goodput today: frequent interruptions that necessitate restarts from the latest checkpoint, slow inline checkpointing that interrupts training, and limited observability that makes it difficult to detect failures. These issues contribute to a significant increase in the time-to-market (TTM) and cost-to-train. There have been several industry publications articulating these issues, e.g., this Arxiv paper. 

Improving ML Goodput

In order to improve ML Goodput, you need to minimize the impact of disruptive events on the progress of the training workload. To resume a job quickly, you can automatically scale down the job, or swap failed resources from spare capacity. At Google Cloud, we call this elastic training. Further, you can reduce workload interruptions during checkpointing and speed up checkpoint loads on failures from the nearest available storage location. We call these capabilities asynchronous checkpointing and multi-tier checkpointing. 

The following picture illustrates how these techniques provide an end-to-end remediation workflow to improve ML Goodput for training. An example workload of nine nodes is depicted with three-way data parallelism (DP) and three-way pipeline parallelism (PP), with various remediation actions shown based on the failures and spare capacity.

You can customize the remediation policy for your specific workload. For example, you can choose between a hotswap and a scaling-down remediation strategy, or to configure checkpointing frequency, etc. A supervisor process receives failure, degradation, and straggler signals from a diagnostic service.  The supervisor uses the policy to manage these events. In case of correctable errors, the supervisor might request an in-job restart, potentially restoring from a local checkpoint. For uncorrectable hardware failures, a hot swap can replace the faulty node, potentially restoring from a peer checkpoint. If no spare resources are available, the system can scale down. These mechanisms ensure training is more resilient and adaptable to resource changes. When a replacement node is available, training scales up automatically to maximize GPU utilization. During scale down and scale up, user-defined callbacks help adjust hyperparameters such as learning rate and batch size. You can set remediation policies using a Python script.

Let’s take a deeper look at the key techniques you can use when optimizing ML Goodput.

Elastic training

Elastic training enhances the resiliency of LLM training by enabling failure sensing and mitigation capabilities for workloads. This allows jobs to automatically continue with remediation strategies including GPU reset, node hot swap, and scaling down the data-parallel dimension of a workload to avoid using faulty nodes, thereby reducing job interruption time and improving ML Goodput. Furthermore, elastic training enables automatic scaling up of data-parallel replicas when replacement nodes become available, maximizing training throughput. 

Watch this short video to see elastic training techniques in action:

Optimized checkpointing

Sub-optimal checkpointing can lead to unnecessary overhead during training and significant loss of training productivity when interruptions occur and previous checkpoints are restored. You can substantially reduce these impacts by defining a dedicated asynchronous checkpointing process and optimizing it to quickly offload the training state from GPU high-bandwidth memory to host memory. Tuning the checkpoint frequency — based on factors such as the job interruption rate and the asynchronous overhead — is vital, as the best interval may range from several hours to mere minutes, depending on the workload and cluster size. An optimal checkpoint frequency minimizes both checkpoint overhead during training operation and computational loss during unexpected interruptions.

A robust way to meet the demands of frequent checkpointing is to leverage three levels of storage: local node storage, e.g., local SSD; peer node storage in the same cluster; and Google Cloud Storage. This multi-tiered checkpointing approach automatically replicates data across these storage tiers during save and restore operations via the host network interface or NCCL (the NVIDIA Collective Communications Library), allowing the system to use the fastest accessible storage option. By combining asynchronous checkpointing with a multi-tier storage strategy, you can achieve quicker recovery times and more resilient training workflows while maintaining high productivity and minimizing the loss of computational progress.

Watch this short video to see optimized checkpointing techniques in action :

These ML Goodput improvement techniques leverage NVIDIA Resiliency Extension, which provides failure signaling and in-job restart capabilities, as well as recent improvements to PyTorch’s distributed checkpointing, which support several of the previously mentioned checkpoint-related optimizations. Further, these capabilities are integrated with Google Kubernetes Engine (GKE) and the NVIDIA NeMo training framework, pre-packaged into a container image and available with an ML Goodput optimization recipe for easy deployment. 

Elastic training in action

In a recent internal case study with 1,024 A3 Mega GPU-accelerated instances (built on NVIDIA Hopper), workload ML Goodput improved from 80%+ to 90%+ using a combination of these techniques. While every workload may not benefit in the same way, this table shows the specific metric improvements and ML Goodput contribution of each of the techniques.

Conclusion

In summary, elastic training and optimized checkpointing, along with easy deployment options, are key strategies to maximize ML Goodput for large PyTorch Training workloads. As seen from the case study above, they can contribute to meaningful ML Goodput improvements and provide significant efficiency savings. These capabilities are customizable and composable through a python script. If you’re running PyTorch GPU training workloads on Google Cloud today, we encourage you to try out our ML Goodput optimization recipe, which provides a starting point with recommended configurations for elastic training and checkpointing. We hope you have fun building and share your feedback!

^{Various teams and individuals within Google Cloud contributed to this effort. Special thanks to – Jingxin Ye, Nicolas Grande, Gerson Kroiz, and Slava Kovalevskyi, as well as our collaborative partners – Jarek Kazmierczak, David Soto, Dmitry Kakurin, Matthew Cary, Nilay Goyal and Parmita Mehta for their immense contributions to developing all of the components that made this project a success.}

^{1. Assuming A3 Ultra pricing for 20,000 GPUs with jobs spanning 8 weeks or longer}

Confidential Computing has redefined how organizations can securely process their sensitive workloads in the cloud. The growth in our hardware ecosystem is fueling a new wave of adoption, enabling customers to use Confidential Computing to support cutting-edge uses such as building privacy-preserving AI and securing multi-party data analytics. 

We are thrilled to share our latest Confidential Computing innovations, highlighting the creative ways our customers are using Confidential Computing to protect their most sensitive workloads including AI workloads.

Building on our foundational work last year, we’ve seen remarkable progress through our deep collaborations with industry leaders including Intel, AMD, and NVIDIA. Together, we’ve significantly expanded the reach of Confidential Computing, embedding critical security features across the latest generations of CPUs, and also extending them to high-performance GPUs. 

Confidential VMs and GKE Nodes with NVIDIA H100 GPUs for AI workloads, in preview

An ongoing, top goal for Confidential Computing is to expand our capabilities for secure computation. 

We unveiled Confidential Virtual Machines on the accelerator-optimized A3 machine series with NVIDIA H100 GPUs last year, which extends hardware-based data protection from the CPU to GPUs. Confidential VMs can help ensure the confidentiality and integrity of artificial intelligence, machine learning, and scientific simulation workloads using protected GPUs while the data is in use. 

These confidential GPUs are now available in preview for Confidential VMs and for Confidential Google Kubernetes Engine (GKE) nodes. They can help create new opportunities for innovation in regulated industries and collaborative AI development. 

“AI and Agentic workflows are accelerating and transforming every aspect of business. As these technologies are integrated into the fabric of everyday operations — data security and protection of intellectual property are key considerations for businesses, researchers and governments,” said Daniel Rohrer, vice president, software product security, NVIDIA. “Putting data and model owners in direct control of their data’s journey — NVIDIA’s Confidential Computing brings advanced hardware-backed security for accelerated computing providing more confidence when creating and adopting innovative AI solutions and services.”

Confidential Vertex AI Workbench, in preview

We are expanding Confidential Computing support on Vertex AI. Vertex AI Workbench customers can now use Confidential Computing to enhance their data privacy needs, and is now in preview. This integration offers greater privacy and confidentiality with just a few clicks.

Confidential Space with Intel TDX (generally available) and NVIDIA H100 GPUs, in preview

We are excited to announce that Confidential Space is now generally available on the general-purpose C3 machine series with Intel® Trust Domain Extensions (Intel® TDX) technology, and coming soon in preview on the accelerator-optimized A3 machine series with NVIDIA H100 GPUs.

Built on our Confidential Computing portfolio, Confidential Space provides a secure enclave, also known as a Trusted Execution Environment (TEE), that Google Cloud customers can use for privacy-focused applications such as joint data analysis, joint machine learning (ML) model training or secure sharing of proprietary ML models. 

Importantly, Confidential Space is designed to protect data from all parties involved — including removing the operator of the environment from the trust boundary along with hardened protection against cloud service provider access. These properties can help organizations harden their products from insider threats, and ultimately provide stronger data privacy guarantees to their own customers.

Confidential GKE Nodes on C3 machines with Intel TDX and built-in acceleration, generally available

Confidential GKE Nodes are now generally available with Intel TDX. These nodes are powered by the general purpose C3 machine series, which run on the 4th generation Intel Xeon Scalable processors (code-named Sapphire Rapids) and have the Intel Advanced Matrix Extensions (Intel AMX) built in and on by default. 

Confidential GKE Nodes with Intel TDX offers nodes an additional isolation layer from the host and hypervisor to protect nodes against a broad range of software and hardware attacks.

“Intel Xeon processors deliver outstanding performance and value for many machine learning and AI inference workloads, especially with Intel AMX acceleration,” said Anand Pashupathy, vice president and general manager, Security Software and Services, Intel. “Google Cloud’s C3 machine series will not only impress with their performance on AI and other workloads, but also protect the confidentiality of the user’s data.”

Confidential GKE Nodes on N2D machines with AMD SEV-SNP, generally available

Confidential GKE nodes are also now generally available with AMD Secure Encrypted Virtualization-Secure Nested Paging (AMD SEV-SNP) technology. These nodes use the general purpose N2D machine series and run on the 3rd generation AMD EPYC™ (code-named Milan) processors. Confidential GKE nodes with AMD SEV-SNP provides security for cloud workloads through assurance that workloads are running and encrypted on secured hardware. 

Confidential VMs on C4D machines with AMD SEV, in Preview

The C4D machine series are powered by the 5th generation AMD EPYC™ (code-named Turin) processors and designed to deliver optimal, reliable, and consistent performance with Google’s Titanium hardware.

Today, we offer global availability of Confidential Compute on AMD machine families such as N2D, C2D, and C3D. We’re happy to share that Confidential VMs on general purpose C4D machine series with AMD Secure Encrypted Virtualization (AMD SEV) technology are in preview today, and will be generally available soon. 

Unlocking new use cases with Confidential Computing

We’re seeing impact across all major verticals where organizations are using Confidential Computing to unlock business innovations.

AiGenomix
AiGenomix is leveraging Google Cloud Confidential Computing to deliver highly differentiated infectious disease surveillance, early detection of cancer, and therapeutics intelligence with a global ecosystem of collaborators in the public and private sector.

“Our customers are dealing with extremely sensitive data about pathogens. Adding relevant data sets like patient information and personalized therapeutics further adds to the complexity of compliance. Preserving privacy and security of pathogens, patients’ genomic and related health data assets is a requirement for our customers and partners,” said Dr. Jonathan Monk, head of bioinformatics, AiGenomix.

“Our Trusted AI for Healthcare solutions leveraging Google Cloud Confidential Computing overcome the barriers to accelerated global adoption by making sure that our assets and processes are secure and compliant. With this, we are able to contribute towards the mitigation of the ever-growing risk emerging from infectious diseases and drug resistance resulting in loss of lives and livelihood,” said Dr. Harsh Sharma, chief AI strategist, AiGenomix.

Google Ads 
Google Ads has introduced confidential matching to securely connect customers’ first-party data for their marketing. This marks the first use of Confidential Computing in Google Ads products, and there are plans to bring this privacy-enhancing technology to more products over time.

“Confidential matching is now the default for any data connections made for Customer Match including Google Ads Data Manager — with no action required from you. For advertisers with very strict data policies, it also means the ability to encrypt the data yourself before it ever leaves your servers,” said Kamal Janardhan, senior director, Product Management, Measurement, Google Ads.

Google Ads plans to further integrate Confidential Computing across more services, such as the new Google tag gateway for advertisers. This update will give marketers conversion tag data encrypted in the browser, by default, and at no extra cost. The Google tag gateway for advertisers can help drive performance improvements and strengthen the resilience of advertisers’ measurement signals, while also boosting security and increasing transparency on how data is collected and processed.

Swift
Swift is using Confidential Computing to ensure that sensitive data from some of the largest banks remains completely private while powering a money laundering detection model.

“We are exploring how to leverage the latest technologies to build a global anomaly detection model that is trained on the historic fraud data of an entire community of institutions in a secure and scalable way. With a community of banks we are exploring an architecture which leverages Google Cloud Confidential Computing and verifiable attestation, so participants can ensure that their data is secure even during computation as they locally train the global model and rely on verifiable attestation to ensure the security posture of every environment in the architecture,” said Rachel Levi, head of artificial intelligence, Swift.

Expedite your Confidential Compute journey with Gemini Cloud Assist, in preview 

To make it easy for you to use Confidential Computing we’re providing AI-powered assistance directly in existing configuration workflows by integrating Gemini Cloud Assist across Confidential Compute, now in preview. 

Through natural language chat, Google Cloud administrators can get tailored explanations, recommendations, and step-by-step guidance for many security and compliance tasks. One such example is Confidential Space, where Gemini Cloud Assist can guide you through the journey of setting up the environment as a Workload Author, Workloads Operator, or a Data Collaborator. This significantly reduces the complexity and the time to set up such an environment for organizations.

Next steps

By continuously innovating and collaborating, we’re committed to making Confidential Computing the cornerstone of a secure and thriving cloud ecosystem. 

Our latest video covers several creative ways organizations are using Confidential Computing to move their AI journeys forward. You can watch it here.