Building Generative AI Applications on Google Cloud Platform - Everything You Need To Know

Generative AI is a subfield of artificial intelligence that focuses on teaching machines to create new data that resembles existing patterns found in training datasets. This isn't just about copying; a well-trained generative AI model can generate entirely new text, images, audio, or video similar to the training data. This has profound implications for various industries, from content creation to drug discovery.

Significance of Generative AI

Generative AI has garnered significant attention due to its ability to autonomously create creative content. Google Cloud Platform (GCP) provides a robust suite of tools and resources to leverage the potential of Generative AI. In this blog, we'll delve into the essentials of Generative AI, its potential use cases, and how to build these applications on GCP.

Source-? https://www.salesforce.com/news/stories/generative-ai-statistics/

Key Concepts in Generative AI

Generative Models

• Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that compete during training. The generator creates data, and the discriminator attempts to distinguish between real and generated data. Over time, the generator improves in creating convincing data.
• Variational Autoencoders (VAEs): VAEs encode and decode data by compressing input data into a lower-dimensional representation and then decoding it back. VAEs are commonly used for tasks like image generation.

Training Data

Generative models require large datasets for training. For instance, an image generator needs a vast collection of images to learn from.

Latent Space

Latent space represents the internal data representation within the generative model, where different data features are disentangled. In GANs, the generator samples points from this latent space to create new data.

Generative AI Use Cases?

Generative AI on Google Cloud Platform (GCP)

Google Cloud Platform provides a comprehensive ecosystem for developing and deploying generative AI models:

1. AI Platform

• Generative AI Studio: A visual development environment for building and deploying generative AI models, including tools for data preparation, model training, and evaluation.
• Vertex AI: A managed machine learning platform offering a unified experience for building, training, and deploying models, with pre-trained generative AI models available.
• Duet AI: An AI collaborator that assists with tasks like data preparation, model training, and deployment.

2. TensorFlow

An open-source machine learning framework developed by Google, popular for building and training generative AI models.

3. BigQuery ML

Allows building machine learning models using SQL directly within BigQuery, useful for quick experimentation and prototyping.

4. Pre-trained Models

GCP offers pre-trained machine learning models, including generative AI models, for various tasks.

GCP Infrastructure Overview

Google Cloud Platform (GCP) is Google's public cloud service, competing with Amazon Web Services and Microsoft Azure. GCP stands out by leveraging Google's robust infrastructure, which manages the traffic and workload of all Google services.

Core Infrastructure Components

Google Compute Engine (Virtual Machines)

Virtual machines (VMs) are often the primary choice when transitioning to the cloud. GCP offers multiple ways to run applications, including App Engine, Container Engine, and Cloud Functions, but VMs are preferred by developers and architects who need full control over their operating system.

• Operating System Support: GCE supports both Linux and Windows VMs. Users can provision VMs using Google-maintained images or import images from their existing infrastructure.
• Image Provisioning: Users can create custom images pre-configured with necessary software, enabling rapid deployment. A common practice includes creating a company-wide compliance image, ensuring consistency across development teams.
• Cost-Saving Strategies: GCP provides several features to reduce costs. Preemptible instances, which can cut VM costs by 80%, are ideal for fault-tolerant applications. Other features include per-minute billing, committed use discounts, sustained use discounts, and a recommendations engine.

Networking

GCP's network infrastructure is designed from scratch to support its vast scale, featuring a global, private, high-speed fiber network with custom routing protocols and hardware. This setup simplifies network management and enhances performance.

• Global Network Objects: GCP allows VMs in different geographical locations (e.g., Hong Kong and Belgium) to communicate over the same private network. Global load balancers can direct traffic to the nearest VM, improving user experience.
• Flexible IP Spaces: VMs within a network can use unrelated subnets, with communication managed by firewall rules.
• Firewall Rules: GCP offers traditional IP-based rules and more flexible network tag-based rules, allowing for simplified traffic management.

Storage

Effective data storage solutions are crucial for application functionality. GCP offers various storage options, including persistent disks, cloud storage, and managed database solutions.

• Persistent Disks: These provide block storage for VMs, available in both HDD and SSD formats, and can be attached to multiple VMs. Local SSDs offer high performance for demanding applications.
• Cloud Storage: This object storage solution supports arbitrary data blobs, which can be versioned, replicated, and shared. It offers multiple storage classes:
• Multi-Regional: Geo-redundant storage, ensuring data safety across multiple data centers.
• Nearline and Coldline: Cost-effective options for infrequently accessed data, balancing storage costs with retrieval costs.

Database Solutions

GCP's database services cater to both NoSQL and relational data needs.

• NoSQL Solutions: Datastore and Bigtable offer scalable, high-performance NoSQL databases.
• Relational Databases: Managed Cloud SQL instances (MySQL or Postgres) and Cloud Spanner provide robust solutions for relational data, with Cloud Spanner uniquely capable of scaling across thousands of servers while maintaining high performance and strong consistency.

These components collectively provide a powerful and flexible foundation for building and deploying applications on GCP.

The GCP Advantage

1. Reliable and Scalable Infrastructure:

• Highly available, cost-effective computing, storage, and networking.
• User-friendly console, robust command-line interface, and extensive APIs.
• Flexibility for hybrid and multi-cloud environments.

2. Optimized Compute and Storage:

• Supports scaling, load balancing, and optimizes data center costs.
• Unified object storage for enterprises and developers.

3. Advanced Networking:

• Enables isolation of cloud infrastructure.
• Scales request-handling capacity and connects physical to private virtual networks.

4. Robust Security Management:

• Cloud Identity and Access Management (IAM) for secure access to services and resources.

5. Comprehensive Data Analytics and AI/ML:

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• Serverless data analytics toolsets for accelerated insights.
• GCP Cloud AI offers unparalleled scale, speed, and ease of use for machine learning.

Introducing BigQuery in GCP: The Core of GCP Database

BigQuery is a petabyte-scale cloud data warehouse integral to the Google Cloud Platform (GCP). It enables users to perform rapid queries on massive datasets, simplifying complex data analysis tasks. Here’s why BigQuery is a preferred choice for many organizations:

• High Performance and Efficiency: BigQuery can handle extensive datasets in seconds, streamlining the analysis process by eliminating the need for manual intervention or complex scripting. This efficiency removes the typical burdens of database management and backups.
• Comprehensive Data Processing: It supports both SQL-based queries and real-time data processing, accommodating both streaming and batch data. This versatility allows for smooth handling of various data types and analysis needs.
• User-Friendly Interface and Unlimited Storage: BigQuery's intuitive graphical user interface (GUI) makes it easy to use, while its unlimited storage capacity ensures that users do not face cost constraints related to data volume.
• Seamless GCP Integration: As a part of GCP, BigQuery integrates seamlessly with other Google Cloud services, providing a robust infrastructure that enhances its functionality and ease of use.
• Scalability and Security: BigQuery scales effortlessly to handle increasing data loads while maintaining high security standards to protect sensitive information.

BigQuery’s architecture is designed to perform large-scale data analysis quickly and securely, making it an excellent tool for businesses aiming to derive insights from their data efficiently.

How to Extract Data from BRM and Establish Relationships in BigQuery

The process of extracting data from the BRM Oracle database and establishing relationships in BigQuery involves several steps:

• Reading the Table Schema: Begin by reading the table schema of the BRM Oracle database to understand its structure.
• Building Relationships: Use the data definition tables of BRM to build relationships between tables by studying the BRM configuration tables. Key columns like POID_ID0 and OBJ_ID0 help identify these relationships.
• Identifying Keys: Identify the primary and foreign keys of each table.
• Creating DDL Statements: Generate DDL statements to create an empty schema in the target database, which can be MySQL, SQLite, or BigQuery.
• Data Import/Export Scripts: Write data import/export scripts in Python. These scripts:

• Loading Data: Load the data into the target databases.
• Using Aarav Solutions InsightForge AI Engine: Once data is loaded, the InsightForge AI engine uses it to serve user queries, facilitating seamless data extraction and relationship management in BigQuery.

By following these steps, organizations can efficiently transfer and manage data from BRM to BigQuery, leveraging its powerful analytical capabilities to enhance their data-driven decision-making processes.

Building Generative AI Models on GCP?

Detailed Steps to Build a Generative AI Application on GCP

Application Hosting Infrastructure

Choose a product to host and serve your application workload, which makes calls to the generative model. Options include:

• Cloud Run: Fully managed compute platform for containerized applications.
• Google Kubernetes Engine (GKE): Managed Kubernetes service.
• Compute Engine: Virtual machines for flexible and scalable hosting.

Model Hosting Infrastructure

Host your generative model using:

• Vertex AI: Comprehensive platform for machine learning.
• Gemini Developer API: For advanced model hosting.
• GKE and Compute Engine: For customizable and portable hosting solutions.

Model Options

Google Cloud provides state-of-the-art foundation models through Vertex AI, including Gemini. Alternatively, deploy third-party models via Vertex AI Model Garden or platforms like HuggingFace Model Hub.

Grounding Solutions

To ensure informed and accurate model responses, grounding involves using real-time data. This can be achieved through:

• Vertex AI Agent Builder
• Vector Search
• AlloyDB for PostgreSQL
• Cloud SQL and BigQuery

Grounding with APIs

Incorporate grounding data from APIs to augment model prompts, ensuring the model’s responses are based on up-to-date and verified information.

Challenges That We Came Across Building Generative AI Applications on GCP And Their Solutions

1. Low Accuracy: Initial models demonstrate low accuracy for generating SQL queries.

Solution: The 'text-unicorn@001' model from the PaLM family improves accuracy but has slower response times.

2. Speed vs. Accuracy Trade-off:

Solution: The Gemini 1.5 Flash model offers faster response times but at the expense of accuracy compared to 'text-unicorn@001'. Prompt engineering techniques are being used to enhance the accuracy of Gemini 1.5 Flash.

3. Impact of LLM Temperature: The temperature parameter in the language model significantly affects the accuracy of generated SQL queries.

4. Query Category and Subcategory Classification:

Solution: A deep learning model classifies queries into categories (e.g., PDF contact matching, BRM database analysis, normal conversation) and subcategories (e.g., product analysis, sales analysis, marketing analysis, audit analysis). The 'textembedding-gecko@003' model is used for embedding test and training data for this classification task.

5. Technical Infrastructure:

Database: Google Cloud Platform's BigQuery is utilized for storing and querying data.

Conclusion

Building generative AI applications on Google Cloud Platform involves understanding key concepts, leveraging powerful tools and services, and following best practices for responsible AI development. With GCP’s robust ecosystem, you can develop, deploy, and scale generative AI models efficiently, unlocking new possibilities across various industries.

Start your Generative AI application development journey today, get in touch with our experts at Aarav Solutions, drop us at mail at [email protected] .