Google Cloud Certified Professional Machine Learning Engineer

سرفصل های دوره

This course will teach you everything you need to know to pass the Google Cloud Certified Professional Machine Learning Engineer exam. You will also gain the skills to develop practical Machine Learning solutions on Google Cloud.

01 Introduction

001 Course Introduction

001 Study Guide.pdf

002 Machine Learning Scenario

003 What Is Machine Learning

004 Understanding GCPs Machine Learning Products

005 Googles Responsible AI Practices

02 Framing Machine Learning Problems

001 Introduction

002 Translating Business Challenges into Machine Learning Use Cases

003 Exploring GCPs Prepackaged Machine Learning Solutions

006 Defining Machine Learning Problems

007 Defining Business Success Criteria

008 Tracking and Running Machine Learning Experiments

009 Identifying Risks to the Feasibility of Machine Learning Solutions

010 Summary

011 Exam Tips

03 Architecting Low-Code Machine Learning Solutions

001 Introduction

002 Matching Machine Learning Services to Their Use Cases

004 Understanding Machine Learning Component Types

005 Demo Removing Sensitive Data with Cloud Data Loss Prevention

006 Exploring and Analyzing Data

008 Automating Orchestrating Serving and Monitoring Machine Learning Projects

009 Choosing Google Cloud Hardware Components

010 Demo Using GPU and TPU Hardware to Accelerate Machine Learning Pipelines

011 Designing Architecture in Compliance with Security Concerns

011 De-identifying sensitive data.txt

012 Summary

013 Exam Tips

04 Collaborating Within and Across Teams to Manage Data and Models

001 Introduction

002 Understanding Data Preparation and Processing

003 Exploring Data with Exploratory Data Analysis (EDA)

004 Demo Prototyping Machine Learning Models with Vertex AI Workbench

005 Building Artificial Intelligence Solutions with Machine Learning APIs

006 Demo Using Document AI to Process Documents at Scale

007 Collaborative Model Prototyping Using Jupyter Notebooks

008 Building Data Pipelines

010 Feature Engineering

011 Demo Creating and Consolidating Features with Vertex AI Feature Store

012 Summary

013 Exam Tips

05 Prototyping and Developing Machine Learning Models

001 Introduction

002 Overview of Building Models

003 Training Models

004 Demo Creating and Serving a Machine Learning Model Using Vertex AI and AutoML

005 Testing Models

006 Demo Building a Machine Learning Model with BigQuery ML

008 Demo Creating a Distributed Vertex AI Job

009 Scaling Model Training and Serving

010 Demo Tuning Hyperparameters

011 Summary

012 Exam Tips

06 Scaling and Serving Models

001 Introduction

002 Designing and Implementing Training Pipelines

003 Implementing Serving Pipelines

005 Tracking and Auditing Metadata

006 Demo Automating Model Training

007 Summary

008 Exam Tips

07 Monitoring Optimizing and Maintaining Machine Learning Solutions

001 Introduction

002 Monitoring and Troubleshooting Machine Learning Solutions

003 Tuning the Performance of Machine Learning Solutions for Training and Serving in Production

004 Summary

005 Exam Tips

08 Practice Exam

001 Preparing for the Exam

09 Conclusion

001 Course Summary

002 Conclusion and Whats Next

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تولید کننده: A Cloud Guru