How to integrate and deploy AI in Google Cloud

Is your cloud infrastructure ready for AI? In part two of this three-part series on AI in Google Cloud, you’ll uncover strategies to integrate and deploy AI in Google Cloud, plus keys to building secure, privacy-compliant AI infrastructure

Missed part 1? Check it out here:

Exploring Google Cloud’s AI Tools

You can integrate and deploy AI in Google Cloud for your existing applications and workflows, whether you’re on the cloud, edge, or hybrid.

There are over 900 partners and software integrations in Google’s data and AI ecosystem.

Integrating AI in Google Cloud

Existing models have already been created to perform tasks for many AI/ML applications. In these cases, it makes the most sense to use a model that’s been created using the best data, compute power, and data science methods available.

This is the value proposition of Google Cloud APIs.

Google’s AI and machine learning APIs help you easily integrate AI in Google Cloud:

Conversational AI APIs
Dialogflow API	Conversational AI platform with intent-based and generative AI LLM capabilities. Build natural, rich conversational experiences into mobile and web apps, smart devices, bots, and more. Create natural interactions for complex multi-turn conversations. Quickly build and deploy advanced agents. Get enterprise-grade scalability. Build a chatbot based on a website or collection of documents.

Document & Data APIs
Document AI API	Process documents with pretrained models for both basic extractors and specialized models. Extract, classify, and split data from documents. Reduce manual document processing and minimize setup costs. Gain insights from document data
Document Warehouse API	Store, search, organize, govern, and analyze documents and their structured metadata. Get fine-grained Access Control (permissions) at the document and folder levels. Manage extracted and tagged metadata.

Generative AI APIs
Foundation Model APIs	Get pre-trained multitask large models that can be tuned or customized for specific tasks using Vertex AI. Handle vision, dialog, code generation, and code completion. Generate text completion, multi-turn chat, and text embeddings. Generate and complete code with Codey. Generate and customize images with Imagen.
Vertex AI Agent Builder API	Provides step-by-step orchestration of enterprise search and conversational applications with pre-built workflows for common tasks like onboarding, data ingestion, and customization. Build a Google-quality search app on your own data. Build multimodal apps that can respond with text and images. Summarize data with tech powered by generative AI.

Image & Video APIs
Vision API	Integrate vision detection features, including image labeling, face and landmark detection, and optical character recognition (OCR). Accurately predict and understand images with ML. Quickly classify images into millions of predefined categories.
Video Intelligence API	Enable powerful content discovery and engaging video experiences. Extract rich metadata at the video, shot, or frame level. Analyze videos with recognition based on 20,000+ objects, places, and actions.

Machine Learning APIs
Vertex AI API	Train, test, and tune high-quality custom ML models with minimal expertise or effort. Deploy 100+ models, including multimodal and foundation models like Gemini.

Speech, Text, & Language APIs
Natural Language API	Derive insights from unstructured text. Apply NLP to applications. Train open ML models to classify, extract, and detect sentiment.
Speech to Text API	Accurately convert speech into text with automatic speech recognition, real-time transcription, and enhanced phone call models.
Text to Speech API	Convert text into natural-sounding speech. Improve customer interactions. Power voice-based UI in devices and applications. Enable personalized communication.
Translation API	Make content and apps multilingual with fast, dynamic machine translation. Translate in real-time. Get compelling localization of content. Internationalize your products.

Deploying AI in Google Cloud

There are four easy steps to deploy a machine learning model in Google Cloud:

Package your model correctly. You have multiple libraries and frameworks to choose from.
Create a Google storage bucket and upload your packaged model. This enables your models to be accessed by Google Cloud.
Create an AI Platform Prediction model resource. This is a container for your ML model versions. It manages your cloud computing resources and allows apps to request model predictions.
Create an AI Platform Prediction version resource. This helps you keep multiple versions of your model without needing to change any application structures.

Building a Secure & Compliant AI Infrastructure

The key to deploying AI in Google Cloud is to harness the power it holds while minimizing security threats.

Google’s built-in features ensure your organization’s security isn’t at risk when you adopt the cloud!

Security threats can impact your AI deployments in many ways, including:

Model manipulation and evasion, e.g. crafting malicious inputs to language models that lead it to generate harmful or inaccurate responses.
Application compromise, e.g. overloading the AI system to disrupt operations or extracting intellectual property or proprietary algorithms via reverse engineering.
AI infrastructure manipulation, e.g. manipulating the infrastructure where the model makes predictions or injecting a trigger during training to do manipulations in the future.
Data exposure, e.g. exposing confidential training data or injecting malicious data during training to skew results toward a specific outcome.

Google Cloud helps organizations achieve security at scale by providing a platform that integrates into your existing user security and data management strategy.

With Google Cloud’s robust security and compliance features, you can build a secure and compliant AI infrastructure that protects your data, applications, and users.

Here are a few ways Google ensures you retain control over your data:

BeyondCorp: Provides access to internal applications from anywhere.
Data Loss Prevention (DLP) API: Identifies and manages sensitive data.
Google Cloud Armor: Protects your apps from DDoS attacks.
Identity-Aware Proxy (IAP): Manages access to your apps without a VPN.
VPC Service Controls: Mitigates data exfiltration risks by creating secure perimeters around your resources.

To help protect your AI models from attacks and maintain integrity, ensure your training data is anonymized and follows data privacy regulations. Tools like TensorFlow Extended (TFX) have integrated security practices that make your pipelines even more secure.

Your AI apps should follow secure coding practices to prevent vulnerabilities. If you're using containers, use GKE security features like GKE Sandbox and Binary Authorization. Regularly scan your apps for vulnerabilities with tools like Google Cloud Security Scanner.

Develop and test an incident response plan to quickly address security incidents. Google Cloud’s Security Command Center can help you detect and respond to threats across your environment. Perform regular security audits and risk assessments to mitigate gaps.

Diving Deeper into Integrating & Deploying AI in Google Cloud

Are you using Google Cloud’s advanced AI to gain a competitive edge?

In our Mastering AI/ML on Google Cloud eBook, dive even deeper into strategies to integrate and deploy AI in Google Cloud with secure, privacy-compliant AI infrastructure. Get the free eBook now.

Jump to part 3 of the blog series here: Google Cloud AI Pricing & Use Cases.