Machine Learning in the Enterprise

سرفصل های دوره

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.

1. Introduction

1. Course introduction

2. Understanding the ML Enterprise Workflow

1. Introduction

2. Overview of an ML enterprise workflow

03. Data in the Enterprise

01. Introduction

02. Feature Store

03. Data Catalog

04. Dataplex

05. Analytics Hub

06. Data preprocessing options

07. Dataprep

08. Lab intro - Exploring and Creating an Ecommerce Analytics Pipeline with Dataprep

04. Science of Machine Learning and Custom Training

01. Introduction

02. The art and science of machine learning

03. Make training faster

04. When to use custom training

05. Training requirements and dependencies (part 1)

06. Training requirements and dependencies (part 2)

07. Training custom ML models using Vertex AI

5. Vertex Vizier Hyperparameter Tuning

1. Introduction

2. Vertex AI Vizier hyperparameter tuning

3. Lab intro - Vertex AI - Hyperparameter Tuning

6. Prediction and Model Monitoring Using Vertex AI

1. Introduction

2. Predictions using Vertex AI

3. Model management using Vertex AI

4. Lab intro - Monitoring Vertex AI Models

7. Vertex AI Pipelines

1. Introduction

2. Prediction using Vertex AI pipelines

3. Lab intro - Vertex AI Pipelines

8. Best Practices for ML Development

2. Best practices for model deployment and serving

3. Best practices for model monitoring

4. Vertex AI pipeline best practices

5. Best practices for artifact organization

10. Series Summary

1. Series summary

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تولید کننده: PluralSight