Unleash the power of generative AI with BigQuery and Vertex AI

Businesses aspire to uncover groundbreaking insights and drive operational efficiencies through the power of AI. Achieving this requires a cutting-edge data and AI platform that provides seamless access to both structured and unstructured enterprise data in a secure and compliant manner.

In an effort to assist organizations in this endeavor, Google is introducing groundbreaking advancements that enhance the integration of data and AI at a larger scale and with increased efficiency using BigQuery and Vertex AI. These innovations enable you to:

• Streamline the application of multimodal generative AI to enterprise data by incorporating Gemini models into BigQuery ML
• Maximize the value derived from unstructured data by expanding the integration of BigQuery with Vertex AI's document processing and speech-to-text APIs
• Harness the power of AI-driven search for your business data through vector search in BigQuery

By seamlessly integrating AI capabilities into your data environment through first-party model integration with BigQuery and Vertex AI, the democratization of generative AI empowers data teams to leverage large language models and unlock a new realm of AI-driven analytics. This streamlined approach not only simplifies and accelerates the process of building AI-powered insights but also ensures enhanced security, all while capitalizing on the scalability and efficiency of BigQuery's serverless architecture.

Simplify generative AI use cases with Gemini models

BigQuery ML empowers users to develop, train, and execute machine learning models within BigQuery using familiar SQL. With a significant increase in the number of prediction and training queries processed annually by Google customers, the utilization of built-in ML in BigQuery has seen a remarkable 250% year-over-year growth.

Today, Google is elevating the capabilities of BigQuery by introducing the integration of Gemini 1.0 Pro through Vertex AI. The Gemini 1.0 Pro model is specifically crafted to handle higher input/output scales and deliver superior result quality across various tasks such as text summarization and sentiment analysis. Accessible through simple SQL statements or BigQuery's embedded DataFrame API directly within the BigQuery console, this integration empowers users to create data pipelines that seamlessly blend structured data, unstructured data, and generative AI models to unlock a new realm of analytical applications. For instance, real-time analysis of customer reviews combined with purchase history and product availability can lead to the generation of personalized messages and offers within BigQuery.

Looking ahead, Google is committed to assisting customers in exploring multimodal generative AI use cases by expanding support for the Gemini 1.0 Pro Vision model. This enhancement enables users to analyze complex data such as images, videos, and more using familiar SQL queries. For example, leveraging the Gemini 1.0 Pro Vision model on a large image dataset in BigQuery allows for the generation of image descriptions, enhanced categorization for improved searchability, annotation of key features, colors, aesthetics, and much more.

Leveraging the power of AI to extract insights from unstructured data

Unstructured data, ranging from images to documents and videos, holds a vast reservoir of untapped potential within enterprise datasets. Yet, deciphering this unstructured data can pose a challenge, hindering the extraction of valuable insights.

Enter BigLake - a revolutionary solution that merges data lakes and warehouses into a unified management framework. This integration empowers organizations to not only analyze, search, and secure unstructured data but also to govern and share it seamlessly. With data volumes on the rise, BigLake's utilization has soared to hundreds of petabytes, enabling customers to delve into image analysis using a diverse array of AI models, including Vertex AI's vision APIs, TensorFlow Hub models, or tailor-made solutions.

Expanding on these capabilities, Google is now extending support to effortlessly extract insights from documents and audio files using Vertex AI's document processing and speech-to-text APIs. These enhancements pave the way for the development of generative AI applications for content creation, classification, sentiment analysis, entity extraction, summarization, embeddings generation, and beyond.

For instance, imagine delving into a comprehensive financial analysis by extrapolating key metrics like revenue, profit, and assets from financial reports and merging them with a historical stock performance dataset in BigQuery. Similarly, enhancing customer service becomes a breeze as you dissect sentiment from customer support call recordings, pinpoint common issues, and correlate these insights with purchase history for a more personalized approach.

Enhance the vector search capabilities of your unstructured data

Recently, Google unveiled the exciting preview of BigQuery vector search seamlessly integrated with Vertex AI, revolutionizing how you can explore similarities within your BigQuery data. This feature, often known as approximate nearest-neighbor search, opens the door to a plethora of innovative data and AI applications like semantic search, similarity detection, and retrieval-augmented generation (RAG) with a large language model (LLM). By leveraging vector search, you can elevate the precision and depth of your AI models, enhancing context comprehension, minimizing ambiguity, ensuring factual precision, and adapting seamlessly to diverse tasks and domains.

Imagine the possibilities for retailers looking to enhance their product recommendations for customers. Picture a shopper browsing through a red dress on the retailer's online store. With vector search, customers can now delve into their stylistic preferences such as color, cut, or occasion with ease. By utilizing this cutting-edge technology, the retailer can suggest similar dresses based on these preferences, even if they lack identical descriptions. This intelligent feature enhances the shopping experience by helping customers find what they desire effortlessly, while enabling retailers to showcase items that align closely with their customers' preferences.

Powered by our advanced text embeddings capabilities and designed to adhere to your AI governance policies and access controls, BigQuery vector search unlocks a new realm of data and AI possibilities, including:

• Retrieval-augmented generation (RAG): Uncover relevant data for any query or task and provide it with context to an LLM. For instance, use a support ticket to identify ten closely-related past cases, and present them to an LLM for a contextual summary and resolution suggestion.
• Semantic search: Discover documents that are semantically similar to a given query, even if they don't share the exact wording. This feature is beneficial for tasks like finding related articles, similar products, or answers to questions.
• Text clustering: Group documents into clusters of similar content. This feature aids in organizing documents, identifying duplicates, and spotting trends within a document collection.
• Summarization: Summarize documents by extracting the most similar ones to the original and highlighting the key points. This functionality is perfect for tasks such as creating executive summaries, abstracts, or condensing news articles.

Connect with us for the future of data and generative AI

When it comes to augmenting your business data with generative AI, we’re just getting started. Let's have a chat on how we can explore your Generative AI use cases to launch your company into the future.