Posts Tagged ‘VertexAI’
[GoogleIO2024] Tune and Deploy Gemini with Vertex AI and Ground with Cloud Databases: Building AI Applications
Vertex AI offers a comprehensive lifecycle for Gemini models, enabling customization and deployment. Ivan Nardini and Bala Narasimhan demonstrated fine-tuning, evaluation, and grounding techniques, using a media company scenario to illustrate practical applications.
Addressing Business Challenges with AI Solutions
Ivan framed the discussion around Symol Media’s issues: rising churn rates, declining engagement, and dropping satisfaction scores. Analysis revealed users spending under a minute on articles, signaling navigation and content quality problems.
The proposed AI-driven revamp personalizes the website, recommending articles based on preferences. This leverages Gemini Pro on Vertex AI, fine-tuned with company data for tailored summaries and suggestions.
Bala explained the architecture, integrating Cloud SQL for PostgreSQL with vector embeddings for semantic search, ensuring relevant content delivery.
Fine-Tuning and Deployment on Vertex AI
Ivan detailed supervised fine-tuning (SFT) on Vertex AI, using datasets of article summaries to adapt Gemini. This process, accessible via console or APIs, involves parameter-efficient tuning for cost-effectiveness.
Deployment creates scalable endpoints, with monitoring ensuring performance. Evaluation compares models using metrics like ROUGE, validating improvements.
These steps, available since 2024, enable production-ready AI with minimal infrastructure management.
Grounding with Cloud Databases for Accuracy
Bala focused on retrieval-augmented generation (RAG) using Cloud SQL’s vector capabilities. Embeddings from articles are stored and queried semantically, grounding responses in factual data to reduce hallucinations.
The jumpstart solution deploys this stack easily, with observability tools monitoring query performance and cache usage.
Launched in 2024, this integration supports production gen AI apps with robust data handling.
Observability and Future Enhancements
The demo showcased insights for query optimization, including execution plans and user metrics. Future plans include expanded vector support across Google Cloud databases.
This holistic approach empowers developers to build trustworthy AI solutions.
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[GoogleIO2024] What’s New in Firebase for Building Gen AI Features: Empowering Developers with AI Tools
Firebase evolves as Google’s app development platform, now deeply integrated with generative AI. Frank van Puffelen, Rich Hyndman, and Marina Coelho presented updates that streamline building, deploying, and optimizing AI-enhanced applications across platforms.
Branding Refresh and AI Accessibility
Frank introduced Firebase’s rebranding, reflecting its AI focus. The new logo symbolizes transformation, aligning with tools that make AI accessible for millions of developers.
Rich emphasized gen AI’s flexibility, enabling dynamic experiences like personalized travel suggestions. Vertex AI, Google Cloud’s enterprise platform, offers global access to models like Gemini 1.5 Pro, with SDKs for Firebase simplifying integration.
Marina showcased Vertex AI’s SDKs for Android, iOS, and web, supporting languages like Kotlin, Swift, and JavaScript. These, available since May 2024, facilitate on-device and cloud-based AI, with features like content moderation.
Frameworks for Production-Ready AI Apps
Genkit, an open-source framework, aids in developing, deploying, and monitoring AI features. It supports RAG patterns, integrating with vector databases like Pinecone.
Data Connect introduces PostgreSQL-backed databases with GraphQL APIs, ensuring type-safe queries and offline support via Firestore. In preview as of May 2024, it enhances data management for AI apps.
App Check’s integration with reCAPTCHA Enterprise prevents unauthorized AI access, bolstering security.
Optimization and Monitoring Tools
Crashlytics leverages Gemini for crash analysis, providing actionable insights. Remote Config’s personalization, powered by Vertex AI, tailors experiences based on user data.
Release Monitoring automates post-release checks, integrating with analytics for safe rollouts. These 2024 features ensure reliable AI deployments.
Platform-Specific Enhancements
iOS updates include Swift-first SDKs and Vision OS support. Android gains automated testing and device streaming. Web improvements ease SSR framework hosting on Google Cloud.
These advancements position Firebase as a comprehensive AI app platform.
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[GoogleIO2024] What’s New in Google Cloud and Google Workspace: Innovations for Developers
Google Cloud and Workspace offer a comprehensive suite of tools designed to simplify software development and enhance productivity. Richard Seroter’s overview showcased recent advancements, emphasizing infrastructure, AI capabilities, and integrations that empower creators to build efficiently and scalably.
AI Infrastructure and Model Advancements
Richard began with Google Cloud’s vertically integrated AI stack, from foundational infrastructure like TPUs and GPUs to accessible services for model building and deployment. The Model Garden stands out as a hub for discovering over 130 first-party and third-party models, facilitating experimentation.
Gemini models, including 1.5 Pro and Flash, provide multimodal reasoning with expanded context windows—up to two million tokens—enabling complex tasks like video analysis. Vertex AI streamlines customization through techniques like RAG and fine-tuning, supported by tools such as Gemini Code Assist for code generation and debugging.
Agent Builder introduces no-code interfaces for creating conversational agents, integrating with databases and APIs. Security features, including watermarking and red teaming, ensure responsible deployment. Recent updates, as of May 2024, include Gemini 1.5 Flash for low-latency applications.
Data Management and Analytics Enhancements
BigQuery’s evolution incorporates AI for natural language querying, simplifying data exploration. Gemini in BigQuery generates insights and visualizations, while BigQuery Studio unifies workflows for data engineering and ML.
AlloyDB AI embeds vector search for semantic querying, enhancing RAG applications. Data governance tools like Dataplex ensure secure, compliant data handling across hybrid environments.
Spanner’s dual-region configurations and interleaved tables optimize global, low-latency operations. These features, updated in 2024, support scalable, AI-ready data infrastructures.
Application Development and Security Tools
Firebase’s Genkit framework aids in building AI-powered apps, with integrations for observability and deployment. Artifact Registry’s vulnerability scanning bolsters security.
Cloud Run’s CPU allocation during requests improves efficiency for bursty workloads. GKE’s Autopilot mode automates cluster management, reducing operational overhead.
Security enhancements include Confidential Space for sensitive data processing and AI-driven threat detection in Security Command Center. These 2024 updates prioritize secure, performant app development.
Workspace Integrations and Productivity Boosts
Workspace APIs enable embedding features like smart chips and add-ons into custom applications. New REST APIs for Chat and Meet facilitate notifications and event management.
Conversational agents via Dialogflow enhance user interactions. These tools, expanded in 2024, foster seamless productivity ecosystems.