Google Professional Machine Learning Engineer Course 2023

Certification Exam Guide

Welcome to the Google Professional Machine Learning Engineer Course! This course is designed to help you prepare for the Google Professional Machine Learning Engineer certification exam.

• Develop a deep understanding of Google Cloud technologies and various ML models and techniques to design, build, and productionize machine learning solutions that address specific business challenges while adhering to responsible AI practices.
• Collaborate effectively with cross-functional teams, including application developers, data engineers, and data governance professionals, to ensure the long-term success of ML models throughout their development, deployment, and maintenance.
• Master the skills required to design, implement, and manage ML architectures, data pipelines, and metric interpretations, as well as optimize model performance through training, retraining, deploying, scheduling, monitoring, and refining models in scalable and efficient ways.
• Learn how to use the Google Cloud Platform (GCP) to build and deploy ML models, including how to use GCP services such as BigQuery, Cloud Storage, Cloud AI Platform, and Cloud Functions to build and deploy ML models.

• Data scientists
• Data engineers
• Machine learning engineers
• Software engineers
• Data analysts
• Data architects
• Business analysts
• Anyone interested in learning about machine learning and Google Cloud Platform

• 1.0 - GCP ML Professional Rough Draft
• 1.1 - Course Introduction
• 1.2 - Terminology
• 1.5 - AI-Enabled Workflows
• 1.6 - AI Builds AI to Write AI
• 1.7 - Teaching MLOps at Scale with GitHub
• 1.10 - Simulations vs Experiment Tracking
• 1.11 - When to Use ML
• 1.12 - Supervised vs Unsupervised Learning (v2)
• 1.13 - Optimization
• 1.14 - Clustering
• 1.15 - MLOps Hierarchy of Needs
• 1.16 - Bespoke System Core Business
• 1.17 - Data Poisoning
• 1.18 - Business Success Criteria
• 1.20 - Conclusion and Next Steps

• 2.1 - Introduction to Course Two
• 2.2 - Terminology
• 2.4 - Cloud Developer Workspace Advantage
• 2.5 - What is Continuous Delivery
• 2.6 - Containerized ML Microservices
• 2.7 - SRE Mindset for MLOps
• 2.8 - Reproducible Workflow
• 2.9 - Learn Continuous Integration
• 2.10 - Heavy vs Light MLOps
• 2.11 - Key Components of MLOps Landscape
• 2.12 - Feature Store and Data Warehouse
• 2.13 - Compute Choice
• 2.20 - Conclusion and Next Steps

• 3.1 - Introduction to Course Three
• 3.2 - Terminology
• 3.3 - Onboarding to GCP
• 3.4 - What is Colab
• 3.5 - Life Expectancy EDA
• 3.6 - Data Science on Windows, Virtualenv, and Pip
• 3.7 - Graphing Data
• 3.8 - Labeling Data
• 3.9 - Mechanical Turk Labeling
• 3.10 - Cleaning Up Data
• 3.11 - Scaling Data
• 3.12 - BigQuery and Colab Pipeline
• 3.15 - Feature Engineering Concepts
• 3.16 - Using Public Datasets
• 3.17 - Exploring Data with Google BigQuery
• 3.20 - Conclusion and Next Steps

• 4.1 - Introduction to Course Four
• 4.2 - Terminology
• 4.5 - Using TensorFlow Playground
• 4.6 - Overfitting vs Underfitting
• 4.7 - Selecting Metrics
• 4.10 - Training TensorFlow with Docker and GPU
• 4.11 - Fine-tuning Raw Ingredients with Hugging Face
• 4.12 - Advantages of Transfer Learning
• 4.15 - Operationalizing Microservices
• 4.16 - Demo: Monitoring and Logging Rust App Engine
• 4.17 - Continuous Integration with Rust and GitHub Actions
• 4.18 - Demo: Unit Testing Rust
• 4.19 - Demo: Copilot-enabled Rust
• 4.20 - Setting Up GCP Workstation with Python
• 4.21 - Demo: Google Cloud Shell
• 4.22 - Demo: Google Cloud Editor
• 4.23 - Demo: Google CLI SDK
• 4.24 - Demo: Google gcloud
• 4.25 - Demo: App Engine Rust Deployment
• 4.26 - Demo: Google App Engine with Golang
• 4.27 - Conclusion and Next Steps

• 5.1 - Introduction to Course Five
• 5.2 - Terminology
• 5.3 - BigQuery Prompt Engineering
• 5.4 - Getting Started with Vertex AI
• 5.5 - Understanding TPUs
• 5.6 - TPUs: Part of the Technology Transition
• 5.7 - PyTorch TPU MNIST
• 5.10 - TensorFlow Serving with Docker and GPU
• 5.11 - Rust Pretrained PyTorch Walkthrough
• 5.12 - Rust Pretrained PyTorch Running
• 5.20 - Conclusion and Next Steps

• 6.1 - Introduction to Course Six
• 6.2 - Terminology
• 6.5 - Data Drift and Taleb
• 6.6 - Load Testing with Locust
• 6.7 - Demo: Auditing via Logs
• 6.8 - Demo: Log Dashboard
• 6.9 - Demo: Cloud Web Security Scanner
• 6.10 - Demo: BigQuery Log Query
• 6.11 - Demo: Load Testing
• 6.12 - The Five Whys
• 6.13 - Using Google Courses
• 6.14 - Rust GPU Hugging Face Translator
• 6.15 - PyTorch Stable Diffusion Rust GPU Demo
• 6.16 - High-Performance PyTorch Rust Demo
• 6.17 - Building CUDA-Enabled Stress Test with Rust and PyTorch
• 6.20 - Conclusion and Next Steps

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