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Pluralsight – Machine Learning in the Enterprise 2024-9

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Course introduction

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Section 1: Understanding the ML Enterprise Workflow2 videos

Introduction

Overview of an ML enterprise workflow

Section 2: Data in the Enterprise8 videos

Introduction

Data Catalog

Feature Store

Analytics Hub

Data preprocessing options

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

Dataprep

Dataplex

Section 3: Introduction1 videos

Course introduction

1mNow Playing

Section 4: Science of Machine Learning and Custom Training6 videos

Make training faster

The art and science of machine learning

When to use custom training

Training requirements and dependencies (part 2)

Training requirements and dependencies (part 1)

Training custom ML models using Vertex AI

Section 5: Vertex Vizier Hyperparameter Tuning2 videos

Lab intro - Vertex AI - Hyperparameter Tuning

Vertex AI Vizier hyperparameter tuning

13m

Section 6: Prediction and Model Monitoring Using Vertex AI3 videos

Lab intro - Monitoring Vertex AI Models

Predictions using Vertex AI

Model management using Vertex AI

Section 7: Vertex AI Pipelines2 videos

Prediction using Vertex AI pipelines

Lab intro - Vertex AI Pipelines

Section 8: Best Practices for ML Development4 videos

Best practices for model deployment and serving

Best practices for model monitoring

Vertex AI pipeline best practices

Best practices for artifact organization

Section 9: Series Summary1 videos

Series summary

About this Lecture

Section: Introduction • Size: 3.05 MB • Duration: 1m

Course Summary

Machine Learning in the Enterprise course. This course provides a practical approach to the machine learning process in organizational environments. In this course, using a real example, you will learn about the challenges of a machine learning team facing different business needs and applications. In this course, you will encounter a real scenario where a machine learning team needs to build machine learning models to solve multiple business problems. The team must make important decisions about the tools and methods they will use to build the model. In this course, you will learn how to prepare data, choose the best model for each problem, and train your models. Also, you will learn about common challenges you face in implementing machine learning models in real-world environments.

Course Content

0 / 29 completed

Section 1: Understanding the ML Enterprise Workflow2 videos

Introduction

Overview of an ML enterprise workflow

Section 2: Data in the Enterprise8 videos

Introduction

Data Catalog

Feature Store

Analytics Hub

Data preprocessing options

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

Dataprep

Dataplex

Section 3: Introduction1 videos

Course introduction

1mNow Playing

Section 4: Science of Machine Learning and Custom Training6 videos

Make training faster

The art and science of machine learning

When to use custom training

Training requirements and dependencies (part 2)

Training requirements and dependencies (part 1)

Training custom ML models using Vertex AI

Section 5: Vertex Vizier Hyperparameter Tuning2 videos

Lab intro - Vertex AI - Hyperparameter Tuning

Vertex AI Vizier hyperparameter tuning

13m

Section 6: Prediction and Model Monitoring Using Vertex AI3 videos

Lab intro - Monitoring Vertex AI Models

Predictions using Vertex AI

Model management using Vertex AI

Section 7: Vertex AI Pipelines2 videos

Prediction using Vertex AI pipelines

Lab intro - Vertex AI Pipelines

Section 8: Best Practices for ML Development4 videos

Best practices for model deployment and serving

Best practices for model monitoring

Vertex AI pipeline best practices

Best practices for artifact organization

Section 9: Series Summary1 videos

Series summary