Good data analytics is more important than ever for making smart, data-driven business decisions. However, wrangling huge datasets into actionable insights can be difficult without the right tools. This is where Model Garden on Google Cloud's Vertex AI platform comes in.
Model Garden provides a vast catalog of ready-to-use AI models for text, vision, language, recommendations, and more that can help extract meaningful signals from raw data.
Read on to understand the key ways integrating these models through Model Garden can enhance analytics.
The Model Garden within Google's Vertex AI platform puts the power of over 130 pre-trained AI models at your fingertips, enabling businesses to enhance analytics and extract deeper insights from their data.
Offering a vast centralized catalog spanning model types like text, vision, recommendations, language, speech, tables, and translation, Model Garden has something to elevate virtually any analytics use case.
Some of the key ways integrating models from the Model Garden catalog can aid analytics efforts include:
Tapping into the Model Garden repository makes it simple for organizations to enrich analytics and gain an information advantage through proven enterprise-grade AI techniques tailored to their business problems. Reduced model development and infrastructure overheads free up resources to extract maximum value from data.
Vertex AI curates a variety of widely used open source models like BERT, T5, and Stable Diffusion into Model Garden. This lets your analytics systems leverage these popular models' capabilities without needing to set them up and run them yourself.
Seamless integration with Vertex AI's MLOps capabilities handles model deployment, scaling, monitoring, etc. This simplicity expands the range of AI techniques you can apply to extract signals from data.
To stay competitive, businesses must transform digitally, and that requires tapping into AI. Manually building custom ML models from scratch is complex.
Model Garden simplifies the process by providing configurable foundation models for text, speech, vision, and recommendations ready to enhance analytics applications. It lets you focus resources on your unique business problems rather than underlying ML infrastructure.
Vertex AI's Deployment Manager lets you take models from Model Garden that are customized to your needs and easily deploy them to production for integration with analytics systems.
It handles provisioning servers, scaling to meet traffic, monitoring, explaining predictions, and more, so you don’t have to worry about ML infrastructure overhead. You can instantly update models as better ones emerge from Model Garden, making it easy to have a continuously improving analytics platform.
While Model Garden expands AI capabilities, tapping the full potential of Google Cloud database and storage services takes analytics to the next level.
Vertex AI integrates tightly with Google Cloud databases like BigQuery, Cloud SQL, and Cloud Spanner for accessing data for model development and deployment.
These databases can handle massive datasets required for accurate analytics, while still ensuring fast performance, reliability, and scalability. Integration with Vertex AI brings advanced ML right to your data.
Likewise, vast storage capacity with high availability offered by Cloud Storage makes it the ideal data lake for analytics data pipelines. Vertex AI natively integrates with Cloud Storage via URIs for datasets, model artifacts and other objects used in the machine learning lifecycle. This simplifies building end-to-end analytics while leveraging Google Cloud's storage backbone.
Use cases include piping raw logs to Cloud Storage, having Vertex AI training models read images for computer vision classification from Cloud Storage buckets, and writing Batch Predictions outputs to a Cloud Storage location for BI tools to consume.
Vertex AI’s Generative AI Studio provides an intuitive way to tap into large language models like PaLM 2 made available through Model Garden. Users can quickly try out prompts without needing to set up any infrastructure. Generative AI Studio handles on-demand deployment of compute resources for model querying. This interactive testing prepares you to integrate generative AI into analytics data pipelines via Vertex AI programmatically.
Users can access various generative foundation models in Generative AI Studio, spanning capabilities like:
When prototyping analytics applications utilizing generative AI techniques, getting immediate interactive feedback accelerates building the right solution. Generative AI Studio allows users to instantly try model queries rather than wait for training jobs to complete. This helps construct the optimal prompts for extracting insights. The queries can then be coded into applications using Vertex AI or Dialogflow APIs.
Foundation models available in Model Garden are models trained on huge, diverse datasets that can be adapted to various downstream tasks through further training. For example, a text foundation model trained on billions of webpages and books has learned general associations in language.
Fine-tuning this model on company documents teaches it the style and terminology to generate business text analytics.
Vertex AI allows users to generate predictions from foundation models directly within Google Cloud Spanner SQL queries. This enables the seamless integration of AI inferences into transactional database operations.
For example, a financial firm could use generative text analytics about customer sentiment from conversation transcripts to fulfill orders, by querying the model within the same transaction as order processing.
Since foundation models have been trained on massive datasets, they are skilled at data manipulation tasks. This makes them suitable as data processing components in ETL pipelines.
For example, an image captioning model from Model Garden can tag training images with descriptive text, while a text summarization model can condense long product reviews. These enrich source data, improving downstream analytics.
The breadth of models available in Vertex AI spanning capabilities like translation, recommendations, speech, vision, and language makes them versatile building blocks for diverse analytics use cases.
Combining these models with Vertex AI's integrated, end-to-end MLOps enables unifying all of this AI functionality onto a single development and serving platform. This saves the tremendous overhead of integrating disparate systems, while still providing the latest AI breakthroughs from Model Garden.
Extracting analytics and decision intelligence from raw data using Vertex AI and Model Garden can transform business processes and applications. But maximizing the return requires navigating the integration of data infrastructure, ML pipelines, and application development.
Promevo provides end-to-end guidance to enterprises throughout this journey as a Google-certified partner solely focused on helping you harness the full potential of Google. Our expertise helps to architect the optimal solutions and roadmaps leveraging Vertex AI's unified data and AI functionality for accelerating analytics and value generation.
Ready to get started? Contact us for more information.
Model Garden is a feature in Vertex AI that allows users to easily discover, share, and deploy machine learning models. Users can browse pre-trained models shared publicly or privately within their organization and deploy them through a user-friendly UI.
Model Garden is available within the Vertex AI service on the Google Cloud Platform. Users can access it through the Model Garden page in the Google Cloud console.
Yes, Vertex AI’s model monitoring capabilities can be enabled for custom models during deployment, providing model quality and drift monitoring for any model running on Google Cloud Vertex AI.
Models that directly serve predictions via API can be deployed in minutes with just a few clicks. Fine-tuning models to tailor them to your use case takes additional time based on dataset complexity, but this process is accelerated by Vertex AI hyperparameter tuning.
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