Source URL: https://cloud.google.com/blog/products/storage-data-transfer/gemini-insights-about-cloud-storage/
Source: Cloud Blog
Title: Understand your Cloud Storage footprint with AI-powered queries and insights
Feedly Summary: Google Cloud Storage is at the core of many customers’ cloud deployment because of its simplicity, affordability and near-infinite scale. But managing millions or billions of objects across numerous projects and with hundreds of developers can be complex, often requiring a team of analysts manually analyzing data for insights. Earlier this year, we introduced the experimental launch of a powerful new capability for Cloud Storage: storage insight generation. Leveraging Gemini, our largest and most capable AI model, storage insight generation lets you ask questions to uncover valuable insights about your Cloud Storage environment.
With storage insight generation you can:
eliminate manual data analysis and get answers rapidly
proactively find potential security and compliance risks
identify possible cost-savings opportunities to optimize your storage spend
Google Cloud is the first hyperscale cloud provider to generate storage insights specific to an environment by querying object metadata and using the power of large language models (LLMs). These AI-powered insights generated directly from your Cloud Storage object metadata give you a new level of control and understanding, even at billions of objects scale. In this blog post, we show you how to get started with storage insight generation, to give you a sense of what AI assistance can do for your storage operations.
Generating storage insights with Gemini
To get started, you need to set up the Storage Insights Dataset, a new feature that collects and centralizes all bucket and object metadata across your Google Cloud projects and regions in a BigQuery linked dataset. Each dataset is refreshed every 24 hours and can retain up to 90 days of historical data. During the dataset setup, you need to select insight generation with the Gemini option.
After the initial setup, you’ll be able to access the enhanced user experience, which includes a short summary of your dataset.
To get started, we offer a pre-curated set of prompts with validated responses. We selected these prompts based on customers’ most common questions. These prompts are also a great starting point from which to craft custom prompts.
Storage Insights start page in the Google Cloud Console UI
When you select any of these curated prompts, you can see verified responses. These responses also include charts, allowing you to translate complex data into clear, visual representations, so you can easily understand, analyze, and share key findings across teams.
Storage Insights response page in the Google Cloud Console UI
You can also use multi-turn chat to dive deeper into insights and run your own interactive analyses. The example below shows a natural language prompt being answered by combining metadata across millions of objects. For every response, the underlying query is also shown, and you can navigate to BigQuery with one click to edit or modify the query.
Storage Insights response page in the Google Cloud Console UI
Trust, accuracy and safety
Despite generative AI’s powerful capabilities, there may be occasional hallucinations. To help you assess the generated answer, we have included multiple informational indicators: every response includes the SQL query for easy validation, curated prompts show a ‘high accuracy’ tag, and we include helpful information about data freshness. You can also use the thumbs up/down indicator to share your feedback about the response.
We use the AI model to convert natural language to appropriate SQL query, query the dataset, and summarize responses. Your object and bucket metadata is completely yours and not used for training Google Cloud’s AI models: we do not store any customer prompts unless you choose to share them through the feedback option. Datasets only contain object and bucket metadata based on your selection of projects and do not have access to object content. Finally, we follow Google’s Responsible AI approach to validate answers from the model and increase content safety.
Simpler and easier storage management
Along with this new capability, Cloud Storage offers a comprehensive set of management features to help you manage your storage more easily and efficiently, at scale. We invite Cloud Storage customers to sign up to start generating insights with Gemini today by reaching out to your Google Cloud account team. To learn more, please watch this video recording of the Google Cloud Next 2024 ‘Managing Cloud Storage at scale with Gemini’ session.
AI Summary and Description: Yes
Summary: The text discusses Google Cloud Storage’s new storage insight generation feature, leveraging the Gemini AI model to simplify data analysis. This innovation allows for rapid identification of security risks, compliance issues, and cost-saving opportunities by querying object metadata, and represents a significant advancement in cloud storage management.
Detailed Description:
The text provides an overview of a new capability introduced by Google Cloud for Cloud Storage users, termed storage insight generation. This feature harnesses the power of the Gemini AI model to facilitate data management and provide actionable insights. The analysis touches on several key points that hold significance for security, compliance, and operational efficiency in cloud environments.
– **Simplification and Efficiency**:
– The new feature addresses the complexities of managing large amounts of data across various projects and teams, particularly when manually analyzing data for insights.
– It eliminates the need for extensive manual data analysis, allowing users to retrieve insights rapidly.
– **Proactive Risk Management**:
– Users can identify potential security and compliance risks, which is crucial for maintaining robust security postures in cloud environments.
– The feature also helps organizations stay compliant with various regulations by revealing compliance issues before they escalate.
– **Cost Optimization**:
– The tool assists in spotting opportunities for cost savings regarding storage expenditures, enabling better financial management in cloud usage.
– **AI-Powered Insights**:
– As the first hyperscale cloud provider to offer such insights through LLMs, Google Cloud sets a new standard in data management.
– The integration with BigQuery for centralized metadata enhances operational visibility and allows for enhanced querying capabilities.
– **User Experience Enhancements**:
– The service provides pre-curated prompts for ease of use, delivering valid responses enriched with visual representations of data.
– Clarity is improved with multi-turn chat functionality to foster deeper analysis of the data insights.
– **Trust and Safety Mechanisms**:
– Responses generated by the AI model come with verification tools, such as SQL queries for easy validation, and indicators of accuracy.
– Transparency in data use is emphasized, assuring users that their metadata is maintained securely without being used for training AI models.
– **Next Steps for Users**:
– Users are encouraged to sign up for the feature through their Google Cloud account team, promoting engagement with innovative cloud management practices.
In conclusion, this development not only enhances cloud storage management but also aligns well with best practices in security and compliance, making it a critical tool for organizations leveraging cloud solutions.