Google Cloud Machine Learning Engineer Certification Prep

Building, Deploying, and Managing Machine Learning Services at Scale

What you'll 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
Requirements
Familiarity with basic cloud concepts
Understanding of some use cases of machine learning
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 productionFundamental concepts of model development, infrastructure management, data engineering, and data governancePreparing data, optimizing storage formats, performing exploratory data analysis, and handling missing dataFeature engineering, data augmentation, and feature encoding to maximize the likelihood of building successful modelsUnderstand 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 DatasetsAutoMLVertex AI WorkbenchesCloud StorageBigQueryCloud DataflowCloud 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Working with Google Cloud

Lecture 3 How to Get Help When You are Stuck

Section 2: Framing Business Problems as Machine Learning Problems

Lecture 4 Identifying Business Problems that Benefit from ML

Lecture 5 Defining ML Success Criteria

Lecture 6 Steps to Building ML Models

Lecture 7 Utilizing ML Models in Production

Section 3: Technical Framing of ML Problems

Lecture 8 Supervised Learning - Classification

Lecture 9 Supervised Learning - Regression

Lecture 10 Unsupervised Learning

Lecture 11 Semi-supervised Learning

Lecture 12 Reinforcement Learning

Lecture 13 ML Model Input Structure

Lecture 14 ML Model Output Structure

Lecture 15 Risks to Successful ML Model Development

Section 4: Machine Learning Training Pipelines

Lecture 16 Overview of ML Pipelines

Lecture 17 3 Steps to Production

Lecture 18 Comprehensive ML Services

Section 5: Machine Learning and Related Google Cloud Services

Lecture 19 Introduction to Vertex AI

Lecture 20 Vetex AI Datasets

Lecture 21 Vertex AI Featurestore

Lecture 22 Vertex AI Workbences

Lecture 23 Vetex AI Training

Lecture 24 Introduction to Cloud Storage

Lecture 25 Introduction to BigQuery

Lecture 26 Introduction to Cloud Dataflow

Lecture 27 Introduction to Cloud Dataproc

Section 6: Machine Learning Infrastructure and Security

Lecture 28 Virtual Machines and Containers

Lecture 29 GPUs and TPUs

Lecture 30 Edge Devices

Lecture 31 Securing ML Models

Lecture 32 Protecting Privacy in ML Models

Section 7: Exploratory Data Analysis and Feature Engineering

Lecture 33 Basic Statistics for Data Exploration

Lecture 34 Encoding Data

Lecture 35 Feature Selection

Lecture 36 Class Imbalance

Lecture 37 Feature Crosses

Lecture 38 TensorFlow Transforms

Section 8: Managing and Preparing Data for Machine Learning

Lecture 39 Organizing and Optimizing Training Sets

Lecture 40 Handling Missing Data

Lecture 41 Handling Outliers in Data

Lecture 42 Avoiding Data Leakage

Section 9: Building Machine Learning Models

Lecture 43 Choosing Models and Frameworks

Lecture 44 Interpretability of Models

Lecture 45 Transfer Learning

Lecture 46 Data Augmentation

Lecture 47 Troubleshooting Models

Section 10: Training and Testing Machine Learning Models

Lecture 48 Training Data File Formats

Lecture 49 Hyperparameter Tuning

Lecture 50 Baselines and Unit Tests

Lecture 51 Distributed Training

Section 11: Machine Learning Serving and Monitoring

Lecture 52 Google Cloud Serving Options

Lecture 53 Scaling Prediction Services

Lecture 54 Performance and Business Quality of Predictions

Lecture 55 Fairness in ML Models

Section 12: Tuning and Optimizing Machine Learning Pipelines

Lecture 56 Optimizing Training Pipelines

Lecture 57 Optimizing Serving Pipelines

Section 13: Tips and Resources

Lecture 58 Exam Strategies and Tips

Lecture 59 Additional Resources to Help Prepare for the Exam

Section 14: Thank you for taking the course!

Lecture 60 Thank you for taking the course!

Section 15: Practice Test

ML Engineers who wish to pass the Google Cloud Professional Machine Learning certification exam.,Beginner machine learning engineers wanting to understand MLOps,Software developers who want to use ML services to use ML as an alternative to coding solutions,Cloud architects who want to understand how to design for machine learning serivces,Data engineers who want to expand their skillset to include machine learning operations,Data analysts and data scientists who want to use machine learning in their work.