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... 16:49
Create Your Own Private Local AI Cloud Stack in Under 20 Minutes

Cole Medin

1+ months ago 9K views

Deploying local AI setups to cloud instances provides flexibility and scalability without compromising privacy. The process involves using a comprehensive package that includes various services like LLMs and automation tools. Special attention is given to maintaining secure endpoints through HTTPS, facilitating access for internal teams. The tutorial guides users through the setup process step-by-step, focusing on firewall configurations and DNS records to ensure smooth operation. By utilizing Digital Ocean, users gain access to cost-effective CPU instances suitable for most AI applications, while also addressing considerations for using larger models requiring dedicated GPUs.
02:59 - 03:15

Digital Ocean provides affordable options for CPU instances, ideal for local AI.

AI cloud services Tech & Hardware
... 12:54
Introducing Azure AI Foundry - Everything you need for AI development

Microsoft Mechanics

1+ months ago 4K views

Azure AI Foundry provides a comprehensive platform for creating agentic solutions, facilitating the entire AI development lifecycle from concept to production. It features a model catalog with thousands of AI models, including offerings from prominent companies like OpenAI and Microsoft, while allowing customization and integration with existing systems. Users can create AI agents equipped with specific tasks and knowledge sources, such as Bing, and utilize Semantic Kernel for orchestration. Built-in monitoring and security features ensure optimized performance and compliance throughout AI application management.
01:59 - 02:11

Details on model deployment options, including serverless APIs and custom models.

Azure AI cloud services Tech & Hardware
... 55:56
.NET AI Community Standup - Exploring AI Templates for Visual Studio

dotnet

1+ months ago 2K views

Image retrieval and AI development consolidate in today's session, focusing on Chroma DB and AI templates in .NET. Alex presents a web app utilizing Azure AI Vision for image embedding storage, offering vectorized search capabilities, while Jordan discusses the integration of AI templates into .NET applications. The conversation touches on the importance of local models and generative AI, emphasizing user experience, template functionality, and rapid prototyping through Azure. Attendees are encouraged to explore AI capabilities and workflows available for developers to enhance application integration with various AI models.
11:06 - 11:12

Discusses leveraging Chroma as a vector database for AI image search.

Microsoft Machine Learning Tech & Hardware
... 1:3:29
Build your code-first app with Azure AI Agent Service

Microsoft Reactor

1+ months ago 1K views

AI agents enhance productivity through automation and semi-autonomy, allowing users to focus on high-value tasks. The progression from large language models to agents provides opportunities for goal-oriented actions, memory management, and multi-agent systems. The Azure AI agent service simplifies development through state management and tool integration, while also addressing security concerns. Demonstrated use cases include generating sales reports from data analysis, showcasing the roles of specific agents, and utilizing tools for data retrieval and visualization. With structured features, Azure’s service allows for scalable and effective AI solutions in various business scenarios.
08:31 - 09:26

Demonstration of a sales agent utilizing SQL queries and visualization tools.

Azure AI cloud services Tech & Hardware
... 53:2
AI Genius Series - Session 5 - Leverage Cloud Native Infra for Intelligent Apps

Microsoft Reactor

1+ months ago 1K views

Exploring intelligent applications leveraging Azure Kubernetes Service (AKS) to enhance AI capabilities in cloud-native environments is pivotal. The integration of AI, such as OpenAI models, streamlines the development and operational processes, allowing for rapid deployment of innovative features. Key themes include transforming traditional applications through AI enhancements, utilizing microservices to facilitate incremental improvements, and leveraging tools like Argo for seamless automation. Emphasizing the role of cloud-native architecture enables improved agility and resilience in application deployment, ensuring organizations can adapt rapidly to changing technological landscapes.
07:20 - 07:46

Discussing Kubernetes microservices to integrate AI capabilities seamlessly.

AI cloud services Education
... 49:12
Build Smarter AI Agents with Azure AI Agent Service | #MVPConnect

Microsoft Reactor

1+ months ago 1K views

AI agents have transformed operational processes, enabling organizations to automate tasks, manage workflows, and enhance decision-making capabilities. With advancements in generative AI models, agents now leverage contextual data to provide tailored solutions based on user inputs. The Azure AI Agent Service offers a robust framework for building and managing AI agents, integrating various data sources and tools for high scalability and security. Key features include multi-agent scenarios, advanced model integration, and powerful capabilities for data analysis, ensuring organizations can deploy intelligent solutions effectively.
03:00 - 03:26

Explaining the features and capabilities of AI agents and Azure AI Agent Service.

Azure AI cloud services Education
... 1:2:12
Build AI Solutions with Azure AI Foundry

Microsoft Reactor

1+ months ago 1K views

Session two of the Azure AI Level Up Tuesdays provided an extensive overview of building AI solutions with Azure AI Foundry. Key topics included a practical demonstration of finding the right models, effective prompt engineering, integrating business data for enhanced performance, and evaluating model outputs. The session highlighted the importance of using Azure AI Services for seamless AI solutions development, while emphasizing real-world application through the fictitious Contoso Outdoor Company, which aims to leverage AI for creating website content efficiently. Participants were encouraged to interact actively and bring up questions in the chat throughout the session.
50:30 - 50:40

Designing robust evaluation systems for generative AI models.

Azure AI cloud services Education
... 19:56
Rahul Kulkarni, AWS | AWS Gen AI Analyst Summit 2024

SiliconANGLE theCUBE

1+ months ago 6K views

Amazon Web Services (AWS) leads in silicon innovation, focusing on optimizing silicon for enhanced performance and cost efficiency. The journey began with the launch of Gravion processors, followed by AI-optimized silicon like Inferentia and Tranium. AWS collaborates closely with independent software vendors to transition applications onto custom silicon, ensuring easy integration. Recent advancements include Tranium 2 for AI training, showcasing AWS's commitment to developing infrastructure that supports massive AI models while continually seeking customer feedback to drive innovation in the AI space.
02:50 - 02:54

Tranium and Inferentia launched to support scalable and efficient AI infrastructure.

AWS AI cloud services Tech & Hardware
... 19:56
Rahul Kulkarni, AWS | AWS Gen AI Analyst Summit 2024

SiliconANGLE theCUBE

1+ months ago 6K views

Amazon Web Services (AWS) leads in silicon innovation, focusing on optimizing silicon for enhanced performance and cost efficiency. The journey began with the launch of Gravion processors, followed by AI-optimized silicon like Inferentia and Tranium. AWS collaborates closely with independent software vendors to transition applications onto custom silicon, ensuring easy integration. Recent advancements include Tranium 2 for AI training, showcasing AWS's commitment to developing infrastructure that supports massive AI models while continually seeking customer feedback to drive innovation in the AI space.
02:50 - 02:54

Tranium and Inferentia launched to support scalable and efficient AI infrastructure.

AWS AI cloud services Tech & Hardware