BigQuery and Vertex AI to explore Gemini 1.0 Pro
Innovation may be stifled by conventional partitions separating data and AI teams. These disciplines frequently work independently and with different tools, which can result in data silos, redundant copies of data, overhead associated with data governance, and budgetary issues. This raises security risks, causes ML deployments to fail, and decreases the number of ML models that make it into production from the standpoint of AI implementation.
It can be beneficial to have a single platform that removes these obstacles in order to accelerate data to AI workflows, from data ingestion and preparation to analysis, exploration, and visualization all the way to ML training and inference in order to maximize the value from data and AI investments, particularly around generative AI.
Google is recently announced innovations that use BigQuery and Vertex AI to further connect data and AI to help you achieve this. They will explore some of these innovations in more detail in this blog post, along with instructions on how to use Gemini 1.0 Pro in BigQuery.
What is BigQuery ML?
With Google BigQuery’s BigQuery ML capability, you can develop and apply machine learning models from within your data warehouse. It makes use of BigQuery’s processing and storing capability for data as well as machine learning capabilities, all of which are available via well-known SQL queries or Python code.
Utilize BigQuery ML to integrate AI into your data
With built-in support for linear regression, logistic regression, and deep neural networks; Vertex AI-trained models like PaLM 2 or Gemini Pro 1.0; or imported custom models based on TensorFlow, TensorFlow Lite, and XGBoost, BigQuery ML enables data analysts and engineers to create, train, and execute machine learning models directly in BigQuery using familiar SQL, helping them transcend traditional roles and leverage advanced ML models directly in BigQuery. Furthermore, BigQuery allows ML engineers and data scientists to share their trained models, guaranteeing that data is used responsibly and that datasets are easily accessible.
Every element within the data pipeline may employ distinct tools and technologies. Development and experimentation are slowed down by this complexity, which also places more work on specialized teams. With the help of BigQuery ML, users can create and implement machine learning models using the same SQL syntax inside of BigQuery. They took it a step further and used Vertex AI to integrate Gemini 1.0 Pro into BigQuery in order to further streamline generative AI. Higher input/output scale and improved result quality are key features of the Gemini 1.0 Pro model, which is intended to be used for a variety of tasks such as sentiment analysis and text summarization.
BigQuery ML allows you to integrate generative models directly into your data workflow, which helps you scale and optimize them. By doing this, bottlenecks in data movement are removed, promoting smooth team collaboration and improving security and governance. BigQuery’s tested infrastructure will help you achieve higher efficiency and scale.
There are many advantages to applying generative AI directly to your data:
- Reduces the requirement for creating and maintaining data pipelines connecting BigQuery to APIs for generative AI models
- Simplifies governance and, by preventing data movement, helps lower the risk of data loss
- Lessens the requirement for managing and writing unique Python code to call AI models
- Allows petabyte-scale data analysis without sacrificing performance
- Can reduce your ownership costs overall by using a more straightforward architecture
In order to perform sentiment analysis on their data, Faraday, a well-known customer prediction platform, had to previously create data pipelines and join multiple datasets. They streamlined the process by giving LLMs direct access to their data, merging it with more first-party customer information, and then feeding it back into the model to produce hyper-personalized content—all inside BigQuery. To find out more, view this sample video.
Gemini 1.0 Pro and BigQuery ML
Create the remote model that reflects a hosted Vertex AI large language model before using Gemini 1.0 Pro in BigQuery. Usually, this process only takes a few seconds. After the model is built, use it to produce text by merging data straight from your BigQuery tables.
Then, to access the Gemini 1.0 Pro via Vertex AI and carry out text-generation tasks, use the ML.GENERATE_TEXT construct. The database record and your PROMPT statement are appended by CONCAT. The prompt parameter that controls response randomness is temperature; the lower the temperature, the more relevant the response will be. The boolean flatten_json_output, when set to true, yields a flat, comprehensible text that has been taken from the JSON response.
What your data can achieve with generative AI
They think that the potential of AI technology for your business data is still largely unrealized. Data analysts’ responsibilities are growing with generative AI, going beyond just gathering, processing, and analyzing massive datasets to include proactively influencing data-driven business impact.
Data analysts can, for instance, use generative models to compile past email marketing data (open rates, click-through rates, conversion rates, etc.) and determine whether personalized offers outperform generic promotions or not, as well as which subject line types consistently result in higher open rates. Analysts can use these insights to direct the model to generate a list of interesting options for the subject line that are specific to the identified preferences. With just one platform, they can also use the generative AI model to create interesting email content.
Early adopters have shown a great deal of interest in resolving a variety of use cases from different industries. For example, the following advanced data processing tasks can be made simpler by using ML.GENERATE_TEXT:
Content generation
Without the need for sophisticated tools, analyze user feedback to create customized email content directly within BigQuery. “Create a marketing email using customer sentiment from [table name] “is a prompt
Summarize
Summarize text that is kept in BigQuery columns, like chat transcripts or online reviews. Example prompt “Combine client testimonials in [table name].”
Enhance data
For a given city name, get the name of the country. Example: “Give me the name of the city in column Y for each zip code in column X.”
Rephrasing
Spelling and grammar in written material, including voice-to-text transcriptions, should be done correctly. “Rephrase column X and add results to column Y” is an example of a prompt.
Feature extraction
Feature extraction is the process of removing important details or terms from lengthy text files, like call transcripts and internet reviews. “Extract city names from column X” is the example given.
Sentiment analysis
Recognize how people feel about particular topics in a text. Example prompt: “Incorporate findings into column Y by extracting sentiment from column X.”
Retrieval-augmented generation (RAG)
Utilizing BigQuery vector search, obtain pertinent data related to a task or question and supply it to a model as context. Use a support ticket, for instance, to locate ten related prior cases that you can pass to a model as context so that it can summarize and offer a solution.
Integrating unstructured data into your Data Cloud is made simpler, easier, and more affordable with BigQuery’s expanded support for cutting-edge foundation models like Vertex AI’s Gemini 1.0 Pro.
Come explore the future of generative AI and data with Google
Refer to the documentation to find out more about these new features. With the help of this tutorial, you can operationalize ML workflows, deploy models, and apply Google’s best-in-class AI models to your data without transferring any BigQuery data. Additionally, you can view a demonstration that shows you how to use BigQuery to build an end-to-end data analytics and AI application that fully utilizes the power of sophisticated models like Gemini, as well as a behind-the-scenes look at the development process. View Google’s most recent webcast on product innovation to find out more about the newest features and how BigQuery ML can be used to create and utilize models with just basic SQL.
