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Embark on a comprehensive 3-hour workshop that guides you through the entire machine learning journey, from concept to production. Learn how to create an open-source template in Python for building and deploying ML components. Begin with an example use case, constructing essential elements such as data preparation, model hyper-training, model training, deployment, and online/batch prediction. Gain hands-on experience unit testing these components in a Python notebook. Advance to deploying the components in a Kubeflow pipeline with orchestration for training and prediction, resulting in a production-ready ML pipeline. Implement the tutorial on Google Cloud Platform with Vertex AI, working with XGBoost and TensorFlow models. By the end of this workshop, acquire practical skills to develop and deploy machine learning solutions from initial concept to final production pipeline.
