Intro

• Join the GCP Study Hub Community
• A note on service name updates
• What even is machine learning?
• Types of Models (By Learning Paradigm)
• Classical ML Models
• What is Deep Learning?
• Summary: Classical ML Models vs. Deep Learning Models
• Regression (Linear and Logistic)
• Classification
• Quiz: Intro to ML

Types of Neural Nets You Need To Know

• Convolutional Neural Networks
• ResNet
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTMs)
• Quiz: Types of Neural Nets

Recommendation Systems

• Recommendation Systems
• Quiz: Recommendation Systems

When ML is not needed

• When ML is not needed
• Quiz: When ML is not needed

Model Development: The Big Picture

• The Training Loop
• Backpropagation and Optimizers
• Train/Test Split
• Common Train/Test Splits
• Validation Set
• Quiz: Model Development (Big Picture)

Loss Functions

• Regression Loss Functions
• Classification Loss Functions
• Quiz: Loss Functions

Data Preparation and Understanding

• Importance of Data Quality
• Data Preparation Overview
• Categorical Label Formats
• Intro to Feature Engineering
• One-Hot Encoding
• Binning
• Feature Crosses
• Problems with Element-Wise Multiplication
• Scaling & Normalization
• Temporal Feature Extraction
• Aggregations
• Feature Selection
• Quiz: Data Preparation and Understanding

Common Data Pitfalls

• Class Imbalance
• Addressing Class Imbalance
• Data Leakage
• Quiz: Common Data Pitfalls

Model Training Core Concepts

• Introduction to Hyperparameters
• Must-Know Hyperparameters
• Bias-Variance Tradeoff
• Underfitting
• Overfitting
• Hyperparameter Tuning
• Important Hyperparameter Trade-offs
• Transfer Learning
• Quiz: Model Training Core Concepts

Environment & Troubleshooting

• Processing Units
• Persistent Training Resources (Machine Types)
• Speeding Up Training with Infrastructure
• Out-of-Memory Errors
• Quiz: Environment and Troubleshooting

Model Evaluation

• K-Fold Cross Validation
• Summary of Data Splitting Strategies
• Purpose of Evaluation Metrics
• Regression Metrics
• Classification Metrics
• Symptoms of Data Leakage in Performance Metrics
• Quiz: Model Evaluation

Model Deployment Overview

• Model Deployment Overview
• Quiz: Model Deployment Overview

Model Monitoring

• Model Monitoring Introduction & Core Concepts
• Data Drift
• Skew (as defined by GCP)
• Updating Datasets
• Data Drift vs. Poor Data Quality
• Quiz: Model Monitoring

ML Pipelines

• Overview of ML Pipelines
• Quiz: ML Pipelines

Responsible AI Practices

• Bias Reduction
• Traceability
• Reproducibility
• Explainability
• Quiz: Responsible AI Practices

GCP's Data and AI Landscape

• Why We're Covering These GCP Services
• Cloud Storage
• Quiz: Cloud Storage
• BigQuery: Intro
• BigQuery: Location Configurations
• BigQuery: Spark/BigQuery Connector
• BigQuery: Admin Console
• Quiz: BigQuery
• Cloud SQL
• Quiz: Cloud SQL
• Firebase and Firestore
• Firebase Cloud Messaging
• Quiz: Firebase and Firestore
• Google Cloud SDK & gcloud CLI
• Quiz: gcloud & Google Cloud SDK
• Data Catalog
• Quiz: Data Catalog
• App Engine
• App Engine as model endpoint gateway
• Quiz: App Engine
• Cloud Run
• Quiz: Cloud Run
• Cloud Logging
• Quiz: Cloud Logging
• Cloud Monitoring
• Quiz: Cloud Monitoring
• Resource Labels
• Quiz: Resource labels
• Cloud Pub/Sub
• Pub/Sub: Publishers, Subscribers, Topics, & Subscriptions
• Pub/Sub: Serving Predictions at Scale
• Quiz: Cloud Pub/Sub
• Dataflow
• Dataflow: Windowing
• Big Data Preprocessing with Pub/Sub + Dataflow
• Quiz: Dataflow
• Dataproc
• Quiz: Dataproc
• Cloud Data Loss Prevention (DLP)
• Quiz: Data Loss Prevention API
• Data Fusion
• Quiz: Data Fusion
• Dataprep
• Quiz: Dataprep
• Cloud Run Functions
• Quiz: Cloud Run Functions
• Cloud Composer
• Quiz: Cloud Composer
• Cloud Build: Intro to CI/CD
• Cloud Build: Intro
• Cloud Build: Cross-Project Deployment
• Cloud Build: CI/CD Pipeline for Model Retraining
• Quiz: Cloud Build
• Compute Engine: Intro
• Compute Engine: VM Location
• Compute Engine: Machine Type
• Quiz: Compute Engine (checkpoint #1)
• Compute Engine: Adding GPUs
• Compute Engine: Deep Learning VMs
• Compute Engine: Backup Methods
• Quiz: Compute Engine (checkpoint #2)
• GCP's Pre-trained AI APIs
• Quiz: Pre-Trained APIs on GCP
• Slurm
• Apache Big Data Ecosystem
• Quiz: Open-source tools

Vertex AI

• Vertex AI: Intro
• Vertex AI Datasets
• Vertex AI Feature Store
• Vertex AI Workbench
• Quiz: Vertex AI #1
• Vertex AI Custom Training Jobs
• Vertex AI AutoML
• AutoML: Chronological Data Splitting
• AutoML: NLP Use Case
• Custom Training vs AutoML vs Pretrained APIs
• Quiz: Vertex AI #2
• Vertex AI Experiments
• Vertex AI Model Registry
• Vertex AI Model Metadata
• Quiz: Vertex AI #3
• Deployment Modes
• Vertex AI Endpoints
• Vertex AI Batch Prediction
• Quiz: Vertex AI #4
• Vertex AI Pipelines: Intro to Pipeline DAGs
• Vertex AI Pipelines: Relationship to Kubeflow and Tensorflow Extended
• Quiz: Vertex AI #5
• Vertex AI Model Monitoring: Intro
• Vertex AI Model Monitoring: Detection Types
• Vertex AI Model Monitoring: Sampling Rate and Frequency
• Vertex AI Model Monitoring: Baseline Data
• Vertex AI Model Monitoring: Multiple Models
• Quiz: Vertex AI #6
• Vertex Explainable AI
• Quiz: Vertex AI #7

BigQuery ML

• BigQuery ML: Intro
• BigQuery ML: Important SQL Commands
• BigQuery ML: Supported Models
• BigQuery ML: Customer Segmentation
• BigQuery ML: Importing Tensorflow Models
• BigQuery ML: Exporting Models
• BigQuery ML: Exporting Models to Dataflow
• BigQuery ML: Retraining
• Quiz: BigQuery ML

Efficient Data Handling with Tensorflow

• Intro to TF Input Pipelines
• Input-Bound Training
• TFRecords
• Prefetching
• Interleave
• Quiz: Efficient Data Handling with Tensorflow #1
• Common Mistakes
• Tensors
• Storage Location Choices
• Updating Code for TPU Training
• Quiz: Efficient Data Handling with Tensorflow #2

Planned Additions

• Additional concepts

Practice Exams (not timed)

• Note about practice questions
• Note about service name updates
• Practice Exam 1
• Practice Exam 2
• Practice Exam 3
• Practice Exam 4
• Practice Exam 5
• Practice Exam 6
• Practice Exam 7
• Randomized Practice Exam (not timed)

Exam Simulator

• Exam Simulator (2 hours, 50 random questions)

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