Building Customer-Facing Gen AI Applications Effectively & Responsibly - MLOps Live #31 with MongoDB

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🎯 Key points for quick navigation: 00:00:00 *🤝 Introduction and Session Overview* - Introduction of the speakers and agenda. - Explanation of the webinar format and how to interact. - Overview of what will be covered: scalability, effective, and responsible building of customer-facing gen AI applications. 00:02:33 *💡 Setting the Context for Gen AI Applications* - The evolution of AI and its impact on businesses. - The importance of choosing the right platform and technology for gen AI applications. - Emphasizing the role of flexible and scalable environments. 08:05:00 *🔄 Unified Data Platforms for AI* - Discussion on the need for a unified data platform. - Importance of storing and accessing complex data types. - Flexibility in connecting various applications, frameworks, and clouds. 13:10:00 *🚀 Beginning the Gen AI Journey* - Poll to understand audience's stage in their gen AI journey. - Examples of successful implementations and potential pitfalls. - Addressing the need for governance, cost control, and risk management. 18:22:00 *📈 Gradual Approach to Customer-Facing Apps* - Step-by-step approach for implementing gen AI in customer-facing applications. - Detailed stages of from call analysis to chatbots. - Case studies and real-world examples of gradual implementation. 22:03:00 *🛠️ Building Effective AI Pipelines* - Breakdown of complex AI application pipelines. - Integration of traditional AI with newer gen AI technologies. - Use cases emphasizing the importance of comprehensive and well-structured AI pipelines. 00:24:21 *🏭 Synchronizing AI Factory Processes* - Overview of multi-dimensional CI/CD process: Synchronizes software, models, and datasets, - Importance of monitoring models for performance, data drift, and possible security risks, - Continuous operation feedback loop ensures adapting to fast changes and risk mitigation. 00:26:14 *🧠 Building an Intelligent Chatbot* - Introduction to the concept and architecture of a smart credit card chatbot, - Utilizing hyper-personalization by fetching financial and personal data, - Query refinement, session loading, and context preservation to streamline responses. 00:30:06 *🔄 Personalization in Chatbots* - Chatbot adapts its tone based on client's age, using a casual tone for younger clients and a formal tone for older clients, - Selection of the best credit card offers tailored to client's preferences and financial history, - Continuous dialogue with emojis and cheerful responses for younger clients versus respectful tones for older clients. 00:35:37 *📊 Data and Algorithm Utilization* - Displaying synthetic client data and credit card details used to personalize recommendations, - Algorithm's ability to match clients with appropriate credit cards based on minimized fees and targeted perks, - Importance of data integrity and management for continuous improvement of models. 00:37:03 *🚀 Starting with Gen AI Applications* - Discussion on moving from experimentation to live applications with limited scale and risk, - Importance of building a foundational data and governance infrastructure for scaling AI applications, - Real-world examples of evolving Cloud and on-prem AI deployments based on client needs and regulatory requirements. 00:47:10 *🎁 Workshop Giveaway and Q&A* - Announcement of workshop giveaway for attendees to help address their specific AI challenges, - Transition to a Q&A session focusing on practical steps and considerations for implementing and scaling Gen AI applications, - Emphasis on the importance of governance, monitoring, and continuous improvement in AI projects. 00:49:56 *📈 Adoption of AI in Banking* - The rise in AI spending among top US banks is justified by expected business benefits. - Implementation time for AI projects varies greatly, with easier in-house projects potentially rolling out within weeks. - Security and data privacy are managed by MongoDB via TTL indexes, encryption, and other client-side security features. 00:52:44 *🛡️ Governance and Security in AI Applications* - Governance and guardrails around AI include monitoring, filtering, and using LLM as judges before producing answers. - Prompt injection and boundary issues are managed through regulatory compliance and personalization while ensuring data privacy. 00:55:53 *🏦 Handling PII and Compliance* - Managing personally identifiable information (PII) requires secure training and fine-tuning models with real client data. - Solutions for handling PII include in-cloud and on-prem deployments, allowing for GDPR compliance and secure data usage. - Enterprises can leverage existing client data to enhance AI applications without breaching regulations. 00:57:39 *💡 Common Challenges in Scaling AI* - Scaling AI applications often boils down to managing costs effectively, particularly in GPU usage. - There is a knowledge gap in how to optimize GPU resources, which can lead to exponentially high costs if not managed. - Proper education on GPU usage can lead to significant cost savings and improved application performance. 00:59:12 *🌐 Resources and Closing Remarks* - Participants are encouraged to explore additional resources, including blogs, case studies, and previous webinar sessions available on the website. - Gratitude is expressed to the presenters and participants for their insightful contributions and questions.