Azure AI Search
Achieve exceptional response quality through a vector database specifically designed for advanced retrieval augmented generation (RAG) and contemporary search functionalities. Emphasize substantial growth with a robust, enterprise-ready vector database that inherently includes security, compliance, and ethical AI methodologies. Create superior applications utilizing advanced retrieval techniques that are underpinned by years of research and proven customer success. Effortlessly launch your generative AI application with integrated platforms and data sources, including seamless connections to AI models and frameworks. Facilitate the automatic data upload from an extensive array of compatible Azure and third-party sources. Enhance vector data processing with comprehensive features for extraction, chunking, enrichment, and vectorization, all streamlined in a single workflow. Offer support for diverse vector types, hybrid models, multilingual capabilities, and metadata filtering. Go beyond simple vector searches by incorporating keyword match scoring, reranking, geospatial search capabilities, and autocomplete features. This holistic approach ensures that your applications can meet a wide range of user needs and adapt to evolving demands.
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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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Azure AI Content Safety
Azure AI Content Safety serves as a robust content moderation system that harnesses the power of artificial intelligence to ensure your content remains secure. By utilizing advanced AI models, it enhances online interactions for all users by swiftly and accurately identifying offensive or inappropriate material in both text and images.
The language models are adept at processing text in multiple languages, skillfully interpreting both brief and lengthy passages while grasping context and meaning.
On the other hand, the vision models excel in image recognition, adeptly pinpointing objects within images through the cutting-edge Florence technology.
Furthermore, AI content classifiers meticulously detect harmful content related to sexual themes, violence, hate speech, and self-harm with impressive detail.
Additionally, the severity scores for content moderation provide a quantifiable assessment of content risk, ranging from low to high levels of concern, allowing for more informed decision-making in content management. This comprehensive approach ensures a safer online environment for all users.
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OneTrust Privacy Automation
Transparency, choice and control are key to trust. Organizations have the opportunity to leverage these moments to build trust, and provide more valuable experiences. People expect greater control over their data. We offer privacy and data governance automation to help organizations better understand and comply with regulatory requirements. We also operationalize risk mitigation to ensure transparency and choice for individuals. Your organization will be able to achieve data privacy compliance quicker and build trust. Our platform helps to break down silos between processes, workflows, teams, and people to operationalize regulatory compliance. It also allows for trusted data use. Building proactive privacy programs that are rooted in global best practice and not just reacting to individual regulations is possible. To drive mitigation and risk-based decision-making, gain visibility into unknown risks. Respect individual choice and integrate privacy and security by default in the data lifecycle.
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