Deploy and Manage Generative AI Models on Google Cloud

Course Prerequisites

• Familiarity with machine learning concepts, including model training, evaluation, and inference.
• Experience with Python and TensorFlow or other ML frameworks for model development.
• Basic understanding of Google Cloud Platform (GCP) and related services like Google Compute Engine, Google Kubernetes Engine (GKE), and BigQuery.
• Some experience with cloud-native technologies, such as Docker, Kubernetes, and serverless computing, would be helpful.
• A basic understanding of generative AI concepts such as GANs (Generative Adversarial Networks), transformers, and autoregressive models is recommended.

Learning Objectives

By the end of the Deploy and Manage Generative AI Models on Google Cloud course, you will be able to:

1. Understand the core concepts behind Generative AI, including GANs, autoregressive models, and transformer-based architectures.
2. Use Google Cloud Vertex AI to build, deploy, and manage machine learning models in a scalable cloud environment.
3. Leverage Google Cloud AI tools like BigQuery ML, AI Platform, and AutoML to accelerate model development and deployment.
4. Deploy Generative AI models for tasks such as text generation, image synthesis, style transfer, and other creative AI applications.
5. Optimize model performance by using Vertex AI Pipelines for model training, versioning, and deployment automation.
6. Utilize TensorFlow, PyTorch, and other AI frameworks on Google Cloud AI Platform to train generative models and manage the full model lifecycle.
7. Integrate AI models into existing applications, utilizing RESTful APIs and cloud-based inference tools for scalable, low-latency access.
8. Monitor and manage the performance of Generative AI models in production environments using Google Cloud Monitoring and Google Cloud Logging.
9. Scale models and workflows using Kubernetes, TensorFlow Serving, and serverless AI functions on Google Cloud.
10. Implement security best practices for deploying AI models on the cloud, ensuring compliance and safeguarding sensitive data.
11. Troubleshoot issues related to model deployment, including performance bottlenecks, data input/output issues, and scaling concerns.
12. Prepare for Google Cloud certifications such as Professional Machine Learning Engineer by gaining hands-on experience with generative AI deployment and cloud-native AI operations.

Target Audience

This course is ideal for:

• Data scientists and machine learning engineers who want to learn how to deploy generative AI models on Google Cloud.
• AI engineers and cloud developers seeking to build and manage generative models for tasks like text generation, image synthesis, and data augmentation.
• Cloud architects who want to understand how to integrate AI and machine learning services into scalable cloud infrastructures.
• AI researchers and developers looking to implement and optimize cutting-edge generative AI models on Google Cloud.
• Technical leads and project managers who need to understand how to deploy and scale Generative AI solutions within their organizations.