Machine Learning in the Enterprise

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases.

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

• Introduction 1min
• Introduction 1min
• Introduction 1min
• Understanding the ML Enterprise Workflow 6mins
• Understanding the ML Enterprise Workflow 6mins
• Understanding the ML Enterprise Workflow 6mins
• Data in the Enterprise 33mins
• Data in the Enterprise 29mins
• Data in the Enterprise 29mins
• Science of Machine Learning and Custom Training 36mins
• Science of Machine Learning and Custom Training 36mins
• Science of Machine Learning and Custom Training 36mins
• Vertex Vizier Hyperparameter Tuning 17mins
• Vertex Vizier Hyperparameter Tuning 17mins
• Vertex Vizier Hyperparameter Tuning 17mins
• Prediction and Model Monitoring Using Vertex AI 16mins
• Prediction and Model Monitoring Using Vertex AI 16mins
• Prediction and Model Monitoring Using Vertex AI 16mins
• Vertex AI Pipelines 5mins
• Vertex AI Pipelines 5mins
• Vertex AI Pipelines 5mins
• Best Practices for ML Development 11mins
• Best Practices for ML Development 11mins
• Best Practices for ML Development 11mins
• Course Summary 0mins
• Course Summary 0mins
• Course Summary 0mins
• Series Summary 3mins
• Series Summary 3mins
• Series Summary 3mins