Deploying and Scaling AI Models with Cloud Run

Deploying machine learning models to production can be challenging, especially when managing infrastructure and scaling to meet demand. This text-based course guides you through the process of packaging, deploying, and auto-scaling your AI models efficiently using Cloud Run. You will transition from running models locally to serving them as robust, production-ready APIs in the cloud, utilizing modern serverless architecture to handle varying workloads automatically. What you'll learn: 1. Understand the foundational concepts of AI inference and serverless container hosting. 2. Package machine learning models using Docker container fundamentals. 3. Deploy containerized models to Cloud Run for instant web accessibility. 4. Configure auto-scaling and manage cold starts for optimal inference performance. 5. Implement basic MLOps practices, including versioning and continuous deployment. 6. Secure your inference endpoints and monitor service health. Starting with essential terminology and container basics, the course guides you through writing efficient inference code, containerizing your model, and configuring Cloud Run for automatic scaling and cost efficiency. This course is designed for beginner developers, data scientists, and aspiring ML engineers who want to deploy their models without managing complex infrastructure. No advanced cloud experience is required. Start reading today to bring your AI models to the cloud with confidence.