Data Science Model Deployments and Cloud Computing on GCP

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll gain hands-on experience with deploying data science models on Google Cloud Platform (GCP) while mastering cloud computing concepts. By the end, you will understand essential cloud tools like Google App Engine, Cloud Functions, and Cloud Run, and you’ll be able to efficiently deploy machine learning models into production environments. You'll also explore how cloud scalability, serverless computing, and containerization impact model deployment, ensuring you can deploy models in various environments seamlessly. You will start by exploring key cloud concepts such as scalability and serverless computing, followed by practical exercises using GCP tools. You'll walk through deploying Python applications, using Docker containers, and setting up continuous deployment pipelines with Cloud Build and GitHub. The course will introduce you to machine learning model lifecycle management and how to use GCP's Vertex AI and Kubeflow for model training and deployment. This course is perfect for data scientists, developers, and cloud enthusiasts looking to apply machine learning models in real-world applications. No advanced cloud experience is required, though basic Python and machine learning knowledge will be beneficial. The course has a hands-on, practical approach to GCP, ensuring you can deploy data science models confidently.