Google Cloud Machine Learning Engineer Certification Prep

Building, Deploying, and Managing Machine Learning Services at Scale

4.29 (666 reviews)
Udemy
platform
English
language
IT Certification
category
instructor
Google Cloud Machine Learning Engineer Certification Prep
5,125
students
5.5 hours
content
Feb 2024
last update
$69.99
regular price

What you will learn

Understand how to use Google Cloud services to build, deploy, and manage machine learning models in production

Use Vertex AI, BigQuery, Cloud Dataflow, and Cloud Dataproc in ML pipelines

Tune training and serving pipelines

Choose appropriate infrastructure, including virtual machines, containers, GPUs and TPUS

How to secure data in ML operations while protecting privacy

Monitor machine learning models in production and know when to retrain models

Explore datasets to identify problems and resolve issues such as class imbalance and insufficient data

Description

Machine Learning Engineer is a rewarding, in demand role, and increasingly important to organizations moving building data intensive services in the cloud.  The Google Cloud Professional Machine Learning Engineer certification is one of the field's most recognized credentials. This course will help prepare you to take and pass the exam.  Specifically, this course will help you understand the details of:


  • Building and deploying ML models to solve business challenges using Google Cloud services and best practices for machine learning

  • Aspects of machine learning model architecture, data pipelines structures, optimization, as well as monitoring model performance in production

  • Fundamental concepts of model development, infrastructure management, data engineering, and data governance

  • Preparing data, optimizing storage formats, performing exploratory data analysis, and handling missing data

  • Feature engineering, data augmentation, and feature encoding to maximize the likelihood of building successful models

  • Understand responsible AI throughout the ML development process and apply proper controls and governance to ensure fairness in machine learning models.

By the end of this course, you will know how to use Google Cloud services for machine learning and just as importantly, you will understand machine learning concepts and techniques needed to use those services effectively.


Unlike courses that set out to teach you how to use particular Google Cloud services, this course is designed to teach you services as well as all the topics covered in the Google Cloud Professional Machine Learning Exam Guide, including machine learning fundamentals and techniques.


The course begins with a discussion of framing business problems as machine learning problems followed by a chapter on the technical framing on ML problems.  We next review the architecture of training pipelines and supporting ML services in Google Cloud, such as:

  • Vertex AI Datasets

  • AutoML

  • Vertex AI Workbenches

  • Cloud Storage

  • BigQuery

  • Cloud Dataflow

  • Cloud Dataproc. 

Machine learning and infrastructure and security are reviewed next.

We then shift focus to building and implementing machine learning models starting with managing and preparing data for machine learning, building machine learning models, and training and testing machine learning models. This is followed by chapters on machine learning serving and monitoring and tuning and optimizing both the training and serving of machine learning models.

Machine learning operations, also known as MLOps, borrow heavily from software engineering practices. As a machine engineer, you will use your understanding of software engineering practices and apply them to machine learning.  Machine learning engineers know how to use ML tools, build models, deploy to production, and monitor ML services. They also know how to tune pipelines and optimize the use of compute and storage resources.   

Machine learning engineers and data engineers complement each other.  Data engineers build services and pipelines for collecting, storing, and managing data while machine learning engineers use those data services as a starting point for accessing data and building ML models to solve specific business problems.



Content

Introduction

Introduction
Working with Google Cloud
How to Get Help When You are Stuck

Framing Business Problems as Machine Learning Problems

Identifying Business Problems that Benefit from ML
Defining ML Success Criteria
Steps to Building ML Models
Utilizing ML Models in Production
Quiz

Technical Framing of ML Problems

Supervised Learning - Classification
Supervised Learning - Regression
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
ML Model Input Structure
ML Model Output Structure
Risks to Successful ML Model Development
Quiz

Machine Learning Training Pipelines

Overview of ML Pipelines
3 Steps to Production
Comprehensive ML Services
Quiz

Machine Learning and Related Google Cloud Services

Introduction to Vertex AI
Vetex AI Datasets
Vertex AI Featurestore
Vertex AI Workbences
Vetex AI Training
Introduction to Cloud Storage
Introduction to BigQuery
Introduction to Cloud Dataflow
Introduction to Cloud Dataproc
Quiz

Machine Learning Infrastructure and Security

Virtual Machines and Containers
GPUs and TPUs
Edge Devices
Securing ML Models
Protecting Privacy in ML Models
Quiz

Exploratory Data Analysis and Feature Engineering

Basic Statistics for Data Exploration
Encoding Data
Feature Selection
Class Imbalance
Feature Crosses
TensorFlow Transforms
Quiz

Managing and Preparing Data for Machine Learning

Organizing and Optimizing Training Sets
Handling Missing Data
Handling Outliers in Data
Avoiding Data Leakage
Quiz

Building Machine Learning Models

Choosing Models and Frameworks
Interpretability of Models
Transfer Learning
Data Augmentation
Troubleshooting Models
Quiz

Training and Testing Machine Learning Models

Training Data File Formats
Hyperparameter Tuning
Baselines and Unit Tests
Distributed Training
Quiz

Machine Learning Serving and Monitoring

Google Cloud Serving Options
Scaling Prediction Services
Performance and Business Quality of Predictions
Fairness in ML Models
Quiz

Tuning and Optimizing Machine Learning Pipelines

Optimizing Training Pipelines
Optimizing Serving Pipelines
Quiz

Tips and Resources

Exam Strategies and Tips
Additional Resources to Help Prepare for the Exam

Thank you for taking the course!

Thank you for taking the course!

Practice Test

Machine Learning Engineer Practice Test

Reviews

Rakesh
September 12, 2023
The practice test is quite basic and good for begineers. However, I think for the certifications need to prepare for some tough question series.
Romil
August 10, 2023
Some sections looked repetitive to me and it had more understanding of Machine learning, I thought there would be more understanding of how ML is to be used on GCP Cloud.
Maria
June 9, 2023
Typos, videos missing, errors in quizzes... The rest is good but not very detailed on the GCP (ML I know and didn't add to my knowledge). Anyway, I will only be able to give a more precise evaluation after taking the exam and comparing it to the content. Thank you.
Alfonso
June 7, 2023
Nice contents, but due to the extension of the course, it should be taken only as a small part of the exam preparation. I suggest to combine this course with the Google's official learning path.
RICARDO
May 31, 2023
it covers many topics which is good, but not in detail, not in the level of complexity as the exam questions
Rahul
March 30, 2023
It's a good match so far. The sections that are upcoming appear to be exactly what I expect from this course. Hope it pays off!
Walter
March 26, 2023
Learn many useful and important ML and GCP in this course. Passed GCP ML exam on 3/25/2023 ! Thank you !
Umair
February 2, 2023
Absolute garbage course, does not go over relevant topics. i can't even answer basic questions from the sample questions Google has provided based on the material in this course. strongly advise against taking it
Patrick
January 31, 2023
Was wanting to learn how to use Googles platform, not cover basic high level concepts, not at all what i thought the course was going to be. Would not recommend this to anyone looking to focus on GCP.
Mauro
January 30, 2023
So far (~33%) excellent. Clear, concise, streamlined and on-topic. Makes relatively complex concepts accessible. It just works if your goal is to learn MLOps, prepare for the Machine Learning Engineer certification, and a bit beyond to acquire skills for the real world.
Omar
January 29, 2023
I was expecting the course to present more examples regarding ML concepts, basically going deeper studying the core concepts. It is a well explained course that presents all the relevant concepts and topics so far, the explanations are clean and easy to follow
Sachin
December 26, 2022
Too much lengthy and probably need some images/graphics as last few courses slides were just read outs.. can use images/infographics etc.. Thanks.
Satish
December 23, 2022
This is good. Need more practice test. Also answer for one of the questions is displayed in the screen. Please remediate that.
Scott
November 18, 2022
It is a good match for me. The way the material is presented by the instructor makes it easy for me to understand.
Eduardo
October 29, 2022
This is not a preparation course, it is an overview of concepts you could find in the exam. It doesn't help you at all how to tackle exam questions. No hands-on practice examples for you, It doesn't cover relevant ML APIs. For me it was too basic. Most of the practice questions aren't compared in difficulty with the sample questions from the certificate site. Waste of money compared with other resources I found later. I consider this an entry level overview, you aren't going to be prepared for the certificate with this.

Charts

Price

Google Cloud Machine Learning Engineer Certification Prep - Price chart

Rating

Google Cloud Machine Learning Engineer Certification Prep - Ratings chart

Enrollment distribution

Google Cloud Machine Learning Engineer Certification Prep - Distribution chart
4878666
udemy ID
9/12/2022
course created date
10/27/2022
course indexed date
Bot
course submited by