Verve Group is an ecosystem of demand and supply technologies fusing data, media, and technology to deliver results and growth to both advertisers and publishers – no matter the screen or location, no matter who, what, or where a customer is. Classifying massive amounts of this unstructured data at scale is the first step in helping to surface relevant, high-quality content to users—and that’s where natural language processing (NLP) comes in.

Verve Group uses the NLP API from Google Cloud’s Vertex AI to fetch data for their internal content classification quality verification and as an additional source for building categorization models. By leveraging the NLP API’s Content Classification models, which are now generally available and offer Google’s latest large language model (LLM) technology, Verve Group powers classification through an updated and expanded training data set with over 1,000 labels and support for 11 languages (Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch join previously-available English). 

Verve Group has been using Google Cloud’s NLP API since day one, because of both the ease of implementation and the quality compared to competing NLP products. With documentation that is “comprehensive and self-explanatory,” the NLP API “allows for fast adoption and implementation, from test models all the way to production,” said Rami Alanko, GM of Verve Group.

Leveraging the NLP API has facilitated Verve Group’s fast go-to-market motions by enabling its customers to quickly discover and classify new content. “Operating on a global level with tens of different regions and languages, we have still been able to maintain high quality for our product and high retention rates with our clients,” Rami shared. “In a recent client case, we achieved 82% improvement in CTR when optimized with content quality measurements enabled by the API. In another client case, we drove brand safety risk down to 0.16% from 4% thanks to classification quality. Along with the new functionalities of the Google NLP, I can only see this trend continuing to strengthen.”

Verve Group is excited to further expand their NLP use cases by leveraging the new Content Classification models, which have already helped them expand their classification inventory, improve the quality and performance of their quality verification for customers, and unlock new use cases for NLP. “Our classification model accuracy improved 41% using Google NLP as a verification partner,” said Rami.  

Additionally, Verve Group is now using the API for metadata analysis on a  large image database. “We browse the database and run the image metadata via our classification. This flow enables us to classify images reliably aligned with our standard classification.  We pretty much use the same data flow for our runtime in-app textual content analysis, therefore allowing for close to real-time consumer engagement,” Rami added.

To learn more about how companies are leveraging NLP API from Google Cloud Vertex AI, click here, and to learn more about Google Cloud’s work with foundation models and generative AI, read The Prompt on Transform with Google Cloud.

At Google Cloud, we continue to invest in key technologies to progress towards our true north star on invisible security: making strong security pervasive and simple for everyone. Our investments are based on insights from our world-class threat intelligence teams and experience helping customers respond to the most sophisticated cyberattacks. Customers can tap into these capabilities to gain perspective and visibility on the most dangerous threat actors that no one else has. 

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), accelerate our ability to help the people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security, they give people access to AI-powered expertise to go beyond what they could do alone. 

At the RSA Conference 2023, we are excited to announce Google Cloud Security AI Workbench, an industry-first extensible platform powered by a specialized, security LLM, Sec-PaLM. This new security model is fine-tuned for security use cases, incorporating our unsurpassed security intelligence such as Google’s visibility into the threat landscape and Mandiant’s frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles. 

Google Cloud Security AI Workbench powers new offerings that can now uniquely address three top security challenges: threat overload, toilsome tools, and the talent gap. It will also feature partner plug-in integrations to bring threat intelligence, workflow, and other critical security functionality to customers, with Accenture being the first partner to utilize Security AI Workbench. 

The platform will also let customers make their private data available to the platform at inference time; ensuring we honor all our data privacy commitments to customers. Because Security AI Workbench is built on Google Cloud’s Vertex AI infrastructure, customers control their data with enterprise-grade capabilities such as data isolation, data protection, sovereignty, and compliance support. 

Preventing threats from spreading beyond the first infection

We already provide best-in-class capabilities to help organizations immediately respond to threats. But what if we could not just identify and contain initial infections, but also help prevent them from happening anywhere else? With our AI advances, we can now combine world class threat intelligence with point-in-time incident analysis and novel AI-based detections and analytics to help prevent new infections. These advances are critical to help counter a potential surge in adversarial attacks that use machine learning and generative AI systems. That’s why we’re excited to introduce:  

• VirusTotal Code Insight uses Sec-PaLM to help analyze and explain the behavior of potentially malicious scripts, and will be able to better detect which scripts are actually threats. 
• Mandiant Breach Analytics for Chronicle leverages Google Cloud and Mandiant Threat Intelligence to automatically alert you to active breaches in your environment. It will use Sec-PaLM to help contextualize and respond instantly to these critical findings.

These new updates build on the existing AI in Google’s industry-leading solutions. For example, Chronicle Security Operations already uses frontline intelligence, integrated reasoning, and machine learning to identify initial infections, prioritize impact, and contain threats. Another example is reCAPTCHA Enterprise, which uses image noising capabilities to help protect your site from adversaries that leverage novel AI advances, greatly enhancing our defenses against bots. 

Adding intelligence to reduce toil

At Google Cloud, we help organizations modernize security wherever they are, in part by simplifying their security tools and controls whenever possible. Advances in generative AI can help reduce the number of tools organizations need to secure their vast attack surface areas and ultimately, empower systems to secure themselves. This will minimize the toil it takes to manage multiple environments, to generate security design and capabilities, and to generate security controls. Today, we’re announcing: 

• Assured OSS will use LLMs to help us add even more open-source software (OSS) packages to our OSS vulnerability management solution, which offers the same curated and vulnerability-tested packages that we use at Google.
• Mandiant Threat Intelligence AI, built on top of Mandiant’s massive threat graph, will leverage Sec-PaLM to quickly find, summarize, and act on threats relevant to your organization. 

These announcements build on existing capabilities that help customers centralize visibility and control, detect targets, and improve security across their platform. For example, Security Command Center (SCC) uses always-on machine learning to detect malicious scripts executing in the customer container environment and immediately alert the customer. In addition, Cloud Data Loss Prevention leverages machine learning to find and classify data, and with Confidential Computing you can collaborate on, train, and deploy sensitive and regulated AI models in the cloud, all while preserving confidentiality. 

Evolving how practitioners do security to close the talent gap 

At Google, we believe that to truly democratize security, we need to first acknowledge that AI will soon usher in a new era for security expertise that will profoundly impact how practitioners “do” security. Most people who are responsible for security — developers, system administrators, SRE, even junior analysts — are not security specialists by training. 

Imagine a world where novices and security experts are paired with AI expertise to free themselves from repetition and burnout, and accomplish tasks that seem impossible to us today. To help power this evolution, we’re embedding Sec-PaLM-based features that can make security more understandable while helping to improve effectiveness with exciting new capabilities in two of our solutions: 

• Chronicle AI: Chronicle customers will be able to search billions of security events and interact conversationally with the results, ask follow-up questions, and quickly generate detections, all without learning a new syntax or schema.
• Security Command Center AI: Security Command Center will translate complex attack graphs to human-readable explanations of attack exposure, including impacted assets and recommended mitigations. It will also provide AI-powered risk summaries for security, compliance, and privacy findings for Google Cloud.

These new releases bolster our existing efforts to tackle these issues through capabilities like IAM Recommender, which suggests permissions better suited to actual usage patterns. We will soon be augmenting this capability to cover organizational policies, further enabling the administrator to help improve the security posture of their organization. In addition, Mandiant Automated Defense applies machine learning to help reduce the repetitive Tier 1 alert triage problem and address alert fatigue. 

Offering availability 

VirusTotal Code Insight, available now in Preview, is our first example of putting Security AI Workbench to work for our customers. We will be rolling out other offerings to trusted testers in coming months, and they will be available in Preview more broadly this summer. Click here for the demo. 

Security AI Workbench, including Sec-PaLM and partner integrations, in addition to the product innovations described in our demo, are all building blocks for a larger effort to elevate security across the ecosystem. So far, that effort: 

• Provides assistive functions to rapidly develop IT generalist talent to Tier 1 security operator status in a way that wasn’t previously feasible. Security Command Center now can summarize threat intelligence insights and findings for Google Cloud, and Chronicle can quickly generate YARA-L rules or other detections. 
• Provides advanced functions such as iterative query and multivariate detection generation, conversational filtering and interaction with results, and smart case awareness to empower advanced Tier 2 and 3 security operators to focus on threat analysis instead of struggling with process and toil. Mandiant Threat Intelligence users now can elevate their core competencies to hunt, investigate, and remediate threats — using the same tools our own Mandiant experts use. 
• Fuses threat intelligence and AI-based analytic capabilities, which are unsurpassed in the market. VirusTotal Code Insight enables security teams to help gain insights and identify threats in suspicious code. This can significantly enhance their ability to detect and mitigate potential attacks.

However, this is just an initial step. We’ll continue to iterate and innovate, and we encourage customers and partners to leverage Security AI Workbench in new and exciting ways. Moving forward, we anticipate many new use cases to emerge over time. 

Building a safer future

While generative AI has recently captured the imagination, Sec-PaLM is based on years of foundational AI research by Google and DeepMind, and the deep expertise of our security teams. This work includes new efforts to expand our partner ecosystem to provide businesses with security capabilities at every layer of the cybersecurity stack. We have only just begun to realize the power of applying generative AI to security, and we look forward to continuing to leverage this expertise for our customers and drive advancements across the security community.

Establishing a mature MLOps practice to build and operationalize ML systems can take years to get right.  We recently published our MLOps framework to help organizations come up to speed faster in this important domain.  

As you start your MLOps journey, you might not need to implement all of these processes and capabilities. Some will have a higher priority than others, depending on the type of workload and business value that they create for you, balanced against the cost of building or buying processes or capabilities. 

To help ML practitioners translate the framework into actionable steps, this blog post highlights some of the factors that influence where to begin, based on our experience in working with customers. 

The following table shows the recommended capabilities (indicated by check marks) based on the characteristics of your use case, but remember that each use case is unique and might have exceptions. (For definitions of the capabilities, see the MLOps framework.)

Your use case might have multiple characteristics. For example, consider a recommender system that’s retrained frequently and that serves batch predictions. In that case, you need the data processing, model training, model evaluation, ML pipelines, model registry, and metadata and artifact tracking capabilities for frequent retraining. You also need a model serving capability for batch serving.

In the following sections, we provide details about each of the characteristics and the capabilities that we recommend for them.

Pilot

Example: A research project for experimenting with a new natural language model for sentiment analysis.

For testing a proof of concept, your focus is typically on data preparation, feature engineering, model prototyping, and validation. You perform these tasks using the experimentation and data processing capabilities. Data scientists want to set up experiments quickly and easily and track and compare them. Therefore, you need the ML metadata and artifact tracking capability in order to debug, to provide traceability and lineage, to share and track experimentation configurations, and to manage ML artifacts. For large-scale pilots, you might also require dedicated model training and evaluation capabilities.

Mission-critical

Example: An equities trading model where model performance degradation in production can put millions of dollars at stake.

In a mission-critical use case, failure with the training process or production model has a significant negative impact on the business (a legal, ethical, reputational, or financial risk). The model evaluation capability is important to identify bias and fairness, as well as to provide explainability of the model. Additionally, monitoring is essential to assess the quality of the model during training and to assess how it performs in production. Online experimentation lets you test newly trained models against the one in production using a controlled environment before you replace the deployed model. Such use cases also need a robust model governance process to store, evaluate, check, release, and report on models and to protect against risks. You can enable model governance by using the model registry and metadata and artifact tracking capabilities. Additionally, datasets and feature repositories provide you with high-quality data assets that are consistent and versioned.

Reusable and collaborative

Example: Customer Analytic Record (CAR) features that are used across various propensity modeling use cases.

Reusable and collaborative assets allow your organization to share, discover, and reuse AI data, source code, and artifacts. A feature store helps you standardize the processes of registering, storing, and accessing features for training and serving ML models. Once features are curated and stored, they can be discovered and reused by multiple data science teams. Having a feature store helps you avoid reengineering features that already exist, and saves time on experimentation. You can also use tools to unify data annotation and categorization. Finally, by using ML metadata and artifacts tracking, you help provide consistency, testability, security and repeatability of the ML workflows. 

Ad hoc retraining

Example: An object detection model to detect various car parts, which needs to be retrained only when new parts are introduced. 

In ad hoc retraining, models are fairly static and you do not retrain them except when the model performance degrades. In these cases, you need data processing, model training, and model evaluation capabilities to train the models. Additionally, because your models are not updated for long periods, you need model monitoring. Model monitoring detects data skews, including schema anomalies, as well as data and concept drifts and shifts. Monitoring also lets you continuously evaluate your model performance, and it alerts you when performance decreases or when data issues are detected. 

Frequent retraining

Example: A fraud detection model that’s trained daily in order to capture recent fraud patterns.

Use cases for frequent retraining are ones where model performance relies on changes in the training data. The retraining might be based on time intervals (for example, daily or weekly), or it could be triggered based on events like when new training data becomes available. For this scenario, you need ML pipelines to connect multiple steps like data extraction, preprocessing, and model training. You also need the model evaluation capability to ensure that the accuracy of the newly trained model meets your business requirements. As the number of models you train grows, both a model registry and metadata and artifact tracking help you keep track of the training jobs and model versions.

Frequent implementation updates

Example: A promotion model with frequent changes to the architecture to maximize conversion rate.

Frequent implementation updates involve changes to the training process itself. That might mean switching to a different ML framework, such as changing the model architecture (for example, LSTM to Attention) or adding a data transformation step in your training pipeline. Such changes in the foundation of your ML workflow require controls to ensure that the new code is functional and that the new model matches or outperforms the previous one. Additionally, the CI/CD process accelerates the time from ML experimentation to production, as well as reducing the possibility for human error. Because the changes are significant, online experimentation is necessary to ensure that the new release is performing as expected. You also need other capabilities such as experimentation, model evaluation, model registry, and metadata and artifact tracking to help you operationalize and track your implementation updates. 

Batch serving

Example: A model that serves weekly recommendations to a user who has just signed up for a video-streaming service.

For batch predictions, there is no need to score in real time. You precompute the scores and you store them for later consumption, so latency is less of a concern than in online serving. However, because you process a large amount of data at a time, throughput is important. Often batch serving is a step in a larger ETL workflow that extracts, pre-processes, scores, and stores data. Therefore, you need the data processing capability and ML pipelines for orchestration. In addition, a model registry can provide your batch serving process with the latest validated model to use for scoring.

Online serving

Example: A RESTful microservice that uses a model to translate text between multiple languages. 

Online inference requires tooling and systems in order to meet latency requirements. The system often needs to retrieve features, to perform inference, and then to return the results according to your serving configurations. A feature repository lets you retrieve features in near real time, and model serving allows you to easily deploy models as an endpoint. Additionally, online experiments help you test new models with a small sample of the serving traffic before you roll the model out to production (for example, by performing A/B testing).

Get started with MLOps using Vertex AI

We recently announced Vertex AI, our unified machine learning platform that helps you implement MLOps to efficiently build and manage ML projects throughout the development lifecycle. You can get started using the following resources: 

• MLOps: Continuous delivery and automation pipelines in machine learning
• Getting started with Vertex AI
• Best practices for implementing machine learning on Google Cloud

^{Acknowledgements: I’d like to thank all the subject matter experts who contributed,  including Alessio Bagnaresi, Alexander Del Toro, Alexander Shires, Erin Kiernan, Erwin Huizenga, Hamsa Buvaraghan, Jo Maitland, Ivan Nardini, Michael Menzel, Nate Keating, Nathan Faggian, Nitin Aggarwal, Olivia Burgess, Satish Iyer, Tuba Islam, and Turan Bulmus. A special thanks to the team that helped create this, Donna Schut, Khalid Salama, and Lara Suzuki, and Mike Pope for his ongoing support.}