Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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D&B Risk Analytics
Globally, teams in risk, procurement, and compliance are under pressure to manage geopolitical risks and business risks. Third-party risks are impacted by the complexity of domestic and international businesses, as well as complex and diverse regulations. It is crucial that companies proactively manage third-party relationships. This cutting-edge platform, powered by D&B Data Cloud's 520M+ Global Business Records with 2B+ annual updates for third-party risks, is an AI-powered solution that mitigates and monitors counterparty risk on a continual basis. D&B Risk Analytics uses best-in class risk data, including alerts for high-risk purchases and match points of more than a billion. This helps to drive informed decisions. Intelligent workflows allow for quick and thorough screening. Receive alerts on key business indicators.
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Fairly
Both AI and non-AI models require effective risk management and oversight to function optimally. Fairly offers a continuous monitoring system designed for robust model governance and oversight. This platform facilitates seamless collaboration between risk and compliance teams alongside data science and cyber security professionals, ensuring that models maintain reliability and security standards. Fairly provides a straightforward approach to staying current with policies and regulations related to the procurement, validation, and auditing of non-AI, predictive AI, and generative AI models. The model validation and auditing process is streamlined by Fairly, which grants direct access to ground truth in a controlled environment for both in-house and third-party models, all while minimizing additional burdens on development and IT teams. This ensures that Fairly's platform not only promotes compliance but also fosters secure and ethical modeling practices. Furthermore, Fairly empowers teams to effectively identify, assess, and monitor risks while also reporting and mitigating compliance, operational, and model-related risks in alignment with both internal policies and external regulations. By incorporating these features, Fairly reinforces its commitment to maintaining high standards of model integrity and accountability.
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OneTrust Data & AI Governance
OneTrust offers a comprehensive Data & AI Governance solution that integrates various insights from data, metadata, models, and risk assessments to create and implement effective policies for data and artificial intelligence. This platform not only streamlines the approval process for data products and AI systems, thereby fostering faster innovation, but also ensures business continuity through ongoing surveillance of these systems, which helps maintain regulatory adherence and manage risks efficiently while minimizing application downtime. By centralizing the definition and enforcement of data policies, it simplifies compliance measures for organizations. Additionally, the solution includes essential features such as consistent scanning, classification, and tagging of sensitive data, which guarantee the effective implementation of data governance across both structured and unstructured data sources. Furthermore, it reinforces responsible data utilization by establishing role-based access controls within a strong governance framework, ultimately enhancing the overall integrity and oversight of data practices.
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