Build a complete machine learning pipeline on a cloud platform, incorporating data collection, preprocessing, model training, testing, and deployment. Use tools like AWS SageMaker, Google AI Platform, or Azure Machine Learning to automate the end-to-end ML workflow. Implement continuous integration (CI) for model training, automated testing for model accuracy, and continuous delivery (CD) for deploying updated models. This project will demonstrate how cloud services can streamline the process of developing, deploying, and managing machine learning models at scale.

Develop a machine learning platform that enables seamless deployment, monitoring, and management of models across multiple cloud providers such as AWS, Azure, and Google Cloud. The platform can include features for model versioning, resource allocation, automated retraining, and model evaluation. It should also offer cost optimization strategies by selecting the best-performing cloud resources, as well as failover mechanisms to ensure high availability and reliability for continuous AI-driven decision-making across different cloud environments.