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- Level: Beginner
- Duration: 02h 12m 26s
- Release date: 2022-03-24
- Author: Google Cloud
- Provider: Pluralsight
Machine Learning in the Enterprise
Description
Content
This course encompasses a real-world practical approach to the ML Workflow: a case study approach that presents an ML team faced with several ML business requirements and use cases.
- Introduction02m
- Course introduction02m
- Understanding the ML Enterprise Workflow06m
- Introduction00m
- Overview of an ML enterprise workflow06m
- Resources Understanding the ML Enterprise Workflow00m
- Data in the Enterprise32m
- Introduction00m
- Feature Store07m
- Data Catalog03m
- Dataplex05m
- Analytics Hub04m
- Data preprocessing options03m
- Dataprep06m
- Lab intro: Exploring and Creating an Ecommerce Analytics Pipeline with Dataprep00m
- Pluralsight: Getting Started with GCP and Qwiklabs04m
- Lab: Exploring and Creating an Ecommerce Analytics Pipeline with Cloud Dataprep v1.500m
- Resources: Data in the Enterprise00m
- Science of Machine Learning and Custom Training36m
- Introduction01m
- The art and science of machine learning07m
- Make training faster08m
- When to use custom training05m
- Training requirements and dependencies (part 1)09m
- Training requirements and dependencies (part 2)04m
- Training custom ML models using Vertex AI02m
- Lab intro: Vertex AI: Custom Training Job and Prediction Using Managed Datasets00m
- Lab: Vertex AI: Custom Training Job and Prediction Using Managed Datasets00m
- Resources: Science of Machine Learning and Custom Training00m
- Resources: The Science of Machine Learning00m
- Vertex Vizier Hyperparameter Tuning17m
- Introduction00m
- Vertex AI Vizier hyperparameter tuning17m
- Lab intro: Vertex Vizier Hyperparameter Tuning00m
- Lab: Vertex AI: Hyperparameter Tuning00m
- Lab: Using Vertex Vizier to Optimize Multiple Objectives00m
- Resources: Vertex Vizier Hyperparameter Tuning00m
- Prediction and Model Monitoring Using Vertex AI16m
- Introduction01m
- Predictions using Vertex AI07m
- Lab: Vertex SDK: Custom Training Tabular Regression Models for Online Prediction and Explainability00m
- Model management using Vertex AI08m
- Lab intro: Vertex AI Model Monitoring00m
- Lab: Monitoring Vertex AI Model00m
- Resources: Prediction and Model Monitoring Using Vertex AI00m
- Vertex AI Pipelines05m
- Introduction00m
- Prediction using Vertex AI pipelines04m
- Lab intro: Vertex AI Pipelines01m
- Lab Introduction and Walkthrough: Vertex AI pipeline00m
- Lab: Introduction to Vertex Pipelines00m
- Lab: Create and Run ML Pipelines with Vertex Pipelines00m
- Resources: Vertex AI Pipelines00m
- Best Practices for ML Development11m
- Introduction00m
- Best practices for model deployment and serving02m
- Best practices for model monitoring03m
- Vertex AI pipeline best practices04m
- Best practices for artifact organization02m
- Resources: Best Practices for ML Development on Vertex AI00m
- Course Summary00m
- Summary00m
- Resource: All quiz questions00m
- Resources: All readings00m
- Resource: All slides00m
- Series Summary03m
- Series summary03m
- Resource: Best practices summary00m
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