Google Cloud Machine Learning Engineer Certification Prep

[mitsumi]

Published 9/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.23 GB | Duration: 4h 19m

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.

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