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- Level: Beginner
- Duration: 02h 55m 23s
- Release date: 2022-03-24
- Author: Google Cloud
- Provider: Pluralsight
How Google Does Machine Learning
Description
Content
What are best practices for implementing machine learning on Google Cloud?
- Introduction to Course and Series04m
- Course series preview03m
- Course introduction01m
- What It Means to be AI-First36m
- Introduction01m
- What is ML?09m
- What kinds of problems can it solve?06m
- Lab intro: Framing a machine learning problem02m
- Lab solutions: Framing a machine learning problem04m
- Infuse your apps with ML04m
- Build a data strategy around ML10m
- Resources: What It Means to Be AI First00m
- How Google Does ML31m
- Introduction01m
- ML surprise04m
- The secret sauce09m
- ML and business processes02m
- The path to ML04m
- A closer look at the path09m
- End of phases deep dive02m
- Resources: How Google Does ML00m
- Machine Learning Development with Vertex AI40m
- Introduction01m
- Moving from experimentation to production10m
- Components of Vertex AI06m
- Pluralsight: Getting Started with GCP and Qwiklabs04m
- Lab intro: Using an image dataset to train an AutoML model00m
- Lab demo: Using an image dataset to train an AutoML model07m
- Lab: Using an Image Dataset to Train an AutoML Model00m
- Lab intro: Training an AutoML video classification model00m
- Lab demo: Training an AutoML video classification model09m
- Lab: Training an AutoML Video Classification Model00m
- Tools to interact with Vertex AI03m
- Resources: Machine Learning Development with Vertex AI00m
- Machine Learning Development with Vertex Notebooks19m
- Introduction00m
- Machine learning development with Vertex Notebooks05m
- (Optional) Lab intro: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model01m
- (Optional) Lab demo: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model13m
- Lab: Vertex AI Model Builder SDK: Training and Making Predictions on an AutoML Model00m
- Resources: Machine Learning Development with Vertex Notebooks00m
- Best Practices for Implementing Machine Learning on Vertex AI11m
- Introduction00m
- Best practices for machine learning development07m
- Data preprocessing best practices02m
- Best practices for machine learning environment setup02m
- Responsible AI Development34m
- Introduction01m
- Overview01m
- Human biases lead to biases in ML models03m
- Biases in data06m
- Evaluating metrics with inclusion for your ML system07m
- Equality of opportunity10m
- How to find errors in your dataset using Facets06m
- Resources: Responsible AI Development00m
- Summary00m
- Summary00m
- Resource: All quiz questions00m
- Resource: All readings00m
- Resource: All slides00m
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