January 30: AI, Security, Analytics, Cloud, 2025 Planning
Microsoft, Google AWS subject matter experts discuss Generative AI - including LLM Model Comparisons, RAG Data Architecture, NIST AI Risk Management Framework, Responsible AI - plus CyberSecurity/Compliance, Data Analytics/Privacy and Application Modernization/Cloud Migration.
Scroll down to see the full and detailed agenda, plus expert speakers. Click on their name to view their Linkedin profile, and session title for additional information. There is time for live interaction/Q&A with the speakers so have your questions ready. CPE credit hours are provided.
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TIME: 9 am ET until Noon
Speakers, Topics, Agenda
9 am ET Ron Gerber, CEO Angelbeat
Angelbeat Overview and Introductory Comments
Ron’s summarizes the day’s agenda, describes how Angelbeat can organize private/custom AI workshops for your organization, and why you should document an Artificial Intelligence Risk Management Framework, based on NIST Standards. Click on image above to watch Gerber’s Videoblog/Podcast the_angel_beat about Top AI Issues in 2025.
9:10 am Microsoft Keynotes
Alfredo Iglesias, Technical Leader, Application Modernization
Claudia Sequera, Azure Cloud Specialist
Microsoft OpenAI Generative AI Partnership Update
Learn about all the new innovations on the Microsoft and OpenAI Partnership.
Practical Insights and GenAI Technical Deployment Recommendations
AI Data Architecture & Governance: RAG, Data Lakes, Data Privacy
Hallucinations/Deepfakes AI-Generated Bad, Wrong, Malicious Output/Content
Strategies to Optimize GenAI Output/Content: Prompt Engineering vs Fine Tuning vs Data Labeling
Hybrid Orchestrations and Models: LLM vs SLM vs Industry-Specific vs Functional-Specific
Rearchitect applications to microservices with modern cloud-native technologies and architectures
Enhance your applications and data to meet your customer needs and drive incremental business growth with built-in infrastructure maintenance, security patching, and scaling.
Embrace AI-Enabled DevOps Tools in Every Step of the Software Development and Deployment Cycle
Address the Complexities Involved in Modernizing Legacy Applications from Mainframe and Midrange systems to Azure
Discuss the transition from monolithic architectures to microservices, emphasizing the benefits of increased agility and scalability.
Highlight the modernization of infrastructure, applications and databases, ensuring they are optimized for current technological demands.
Explore how GenAI tools can accelerate the entire modernization process, reducing time and costs while enhancing overall efficiency.
9:50 am AWS Keynotes
Tony Santiago, Global Lead GenAI/ML, Senior Partner Solution Architect
Aruun Kumar, Senior Cloud Application Architect
Amazon Bedrock, the Easiest Way to Build and Scale Generative AI Applications with Foundation Models
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) and large language models (LLMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources.
LLMs are limited by the knowledge contained in their static training data. Retrieval-augmented generation (RAG) is a technique that enables LLMs to access and incorporate domain specific data or proprietary information securely from various knowledge sources, significantly extending their capabilities. RAG retrieves relevant passages from a knowledge base and provides them as additional context to the LLM, allowing it to generate outputs grounded in current, factual information.
Amazon Bedrock agents further enhance RAG by orchestrating multistep workflows involving foundation models, knowledge bases, and API calls. Agents can break down complex user queries, retrieve pertinent data via RAG, reason over the retrieved information, and generate comprehensive responses - all without manual engineering. The agent traces its reasoning steps, enabling developers to refine its behavior.
Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.
Build Intelligent, Secure, Scalable Applications In Minutes, with AWS App Studio and NLP
AWS App Studio is a generative AI–powered service that uses natural language processing (NLP) to build business applications, empowering a new set of builders to create applications in minutes. This is the evolution of low and no-code application development, using AI to generate code and without knowledge of programming languages.
With App Studio, technical professionals such as IT Project Managers, Data Engineers, Enterprise Architects, and Solution Architects can quickly develop applications tailored to their organization's needs—without requiring deep software development skills.
10:30 am Renatto Garro, CTO Corporate & Digital Natives, Google
Bring generative AI to real-world experiences quickly, efficiently, and responsibly, powered by Google’s most advanced technology and models including Gemini. Here are some of the specific topics to be addressed:
Build Applications and Experiences Powered by Generative AI: With Vertex AI, you can interact with, customize, and embed foundation models into your applications. Access foundation models on Model Garden, tune models via a simple UI on Vertex AI Studio, or use models directly in a data science notebook. Plus, with Vertex AI Agent Builder developers can build and deploy AI agents grounded in their data.
Customize and Deploy Gemini Models to Production in Vertex AI: Gemini, a multimodal model from Google DeepMind, is capable of understanding virtually any input, combining different types of information, and generating almost any output. Prompt and test Gemini in Vertex AI using text, images, video, or code. With Gemini’s advanced reasoning and generation capabilities, developers can try sample prompts for extracting text from images, converting image text to JSON, and even generate answers about uploaded images.
New Generation of AI Assistants for Developers, Google Cloud Services, and Applications: Gemini Code Assist offers AI-powered assistance to help developers build applications with higher velocity, quality, and security in popular code editors like VS Code and JetBrains, and on developer platforms like Firebase. Built with robust enterprise features, it enables organizations to adopt AI assistance at scale while meeting security, privacy, and compliance requirements. Additionally, Gemini for Google Cloud offerings assist users in working and coding more effectively, gaining deeper data insights, navigating security challenges, and more.