Azure OpenAI Landing Zone reference architecture
In this article, delve into the synergy of Azure Landing Zones and Azure OpenAI Service, building a secure and scalable AI environment. Unpack the Azure OpenAI Landing Zone architecture, which integrates numerous Azure services for optimal AI workloads. Explore robust security measures and the significance of monitoring for operational success. This journey of deploying Azure OpenAI evolves alongside Azure's continual innovation.Security Best Practices for GenAI Applications (OpenAI) in Azure
This article presents an in-depth guide on security best practices for GenAI applications that use LLM models within the Azure platform. Aimed at developers and system administrators, it explores the essentials for maintaining the confidentiality, integrity, and availability of LLMs such as Azure OpenAI. It delves into practical measures for addressing security challenges, including data breaches, misuse of AI, and regulatory compliance, while also emphasizing the role of a shared responsibility model in cloud security. The guide provides a comprehensive roadmap for implementing layered security strategies, encryption protocols, access controls, and monitoring practices to ensure the robust security of LLM applications in Azure.Empowering AI: Building and Deploying Azure AI Landing Zones with Terraform
Discover the power of deploying Azure AI Landing Zones with Terraform. Explore best practices, secure connectivity, and streamlined access to Azure AI services. Learn to create a strong cloud foundation, optimize performance, and ensure governance for your AI solutions. Join us on this practical journey to harness the true capabilities of AI.
Leverage Large Language Models such as ChatGPT for your Ecommerce System
This solution guide presents a novel approach that leverages Large Language Models, such as ChatGPT, to address the limitations found in traditional recommendation methods. Traditional methods are typically task-specific and therefore require corresponding data to train distinct models for various applications. These methods often lack generalization capabilities and underperform in cold start situations.Demystifying Azure OpenAI Networking for Secure Chatbot Deployment
Embark on a technical exploration of Azure's networking features for building secure chatbots. In this article, we'll dive deep into the practical aspects of Azure's networking capabilities and their crucial role in ensuring the security of your OpenAI deployments. With real-world use cases and step-by-step instructions, you'll gain practical insights into optimizing Azure and OpenAI for your projects.Reducing the Environmental Impact of Generative AI: a Guide for Practitioners
Discover how to harness the power of generative AI while minimising its environmental impact in this comprehensive guide for technical practitioners. From selecting the right foundation models and optimising fine-tuning processes to leveraging retrieval augmented generation (RAG) and mastering prompt engineering, we explore key strategies for sustainable AI development and deployment. By following best practices and making informed decisions at each stage of the AI lifecycle, you can effectively integrate generative AI into your products, services, and operations while prioritising energy efficiency, reducing carbon footprint, and promoting responsible resource management. Dive in to learn how to unlock the potential of generative AI without compromising on sustainability.Optimize Azure OpenAI Applications with Semantic Caching
One of the ways to optimize cost and performance of LLMs is to cache the llm responses, this is sometimes referred to as “semantic caching”. In this blog, we will discuss the approaches, benefits, common scenarios and key considerations for using semantic caching.Azure OpenAI and Call Center Modernization
Much has been written about the powerful use cases enabled by Azure OpenAI. One such example is the transformation of the modern call center as their operators evaluate the potential of using artificial intelligence and natural language processing to optimize their processes and improve customer interactions. In this article, we describe a deployment where Azure OpenAI serves as a platform to assist human agents. Existing systems such as conversational chatbots, interactive voice response (IVR), and customer relationship management (CRM) all remain and complement the new system.Data Validation at Scale with Azure Synapse
In the world of Artificial Intelligence and Machine Learning, data quality is paramount in ensuring our models and algorithms perform correctly. By leveraging the power of Spark on Azure Synapse, we can perform detailed data validation at a tremendous scale for your data science workloads.
