Introduction
Course and Google Professional Machine Learning Engineer exam overview
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Framing ML problems: Key terminology
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1. Translating Business Challenges into ML Use Cases
Building AI-enabled workflows
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Using AI tools to build AI tools
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Teaching MLOps at scale with GitHub
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2. Defining ML Problems
Simulations vs. experiment tracking
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When to use ML
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Supervised vs. unsupervised ML
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Optimization
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Clustering
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3. Defining Business Success Criteria
Defining business success criteria
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4. Identifying Risks to Feasibility of ML Solutions
MLOps hierarchy of needs
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Hidden costs of bespoke systems
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Data poisoning
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5. Conclusion: Framing ML Problems
Framing ML problems: Next steps
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6. Introduction: Architecting a ML Solution
Architecting a ML solution: Overview
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Architecting a ML solution: Key terminology
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Cloud developer workspace advantage
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7. Designing a Reliable, Scalable, and Highly Available ML Solution
What is continuous delivery?
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Containerized ML microservices
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SRE mindset for MLOps
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Reproducible workflow
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Learn continuous integration
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8. Choosing Appropriate Google Cloud Hardware Components
Selecting heavy vs. light MLOps
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Key components of MLOps landscape
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Feature store vs. data warehouse
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Compute choice
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9. Conclusion: Architecting a ML Solution
Architecting a ML solution: Next steps
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10. Introduction: Designing Data Preparation and Processing Systems
Designing data preparation and processing systems: Overview
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Designing data preparation and processing systems: Key terminology
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Onboard to GCP
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11. Exploring Data
What is Google Colab?
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Exploratory data analysis for life expectancy
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Data science setup with virtualenv and pip on Windows
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Graphing data for exploratory data analysis
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12. Building Data Pipelines
Labeling data
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Mechanical Turk labeling
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Cleaning up data
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Scaling data
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BigQuery data pipelines with Colab
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13. Creating Input Features
Feature engineering concepts
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Extracting features from public datasets
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Exploratory data analysis with Google BigQuery
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14. Conclusion: Designing Data Preparation and Processing Systems
Designing data preparation and processing systems: Next steps
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15. Introduction: Developing ML Models
Developing ML models: Overview
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Developing ML models: Key terminology
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16. Building Models
Using TensorFlow Playground
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Overfitting vs. underfitting
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Selecting the right metrics
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17. Training Models
Training models with TensorFlow and a GPU-enabled Docker
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Fine-tuning raw ingredients with Hugging Face
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Advantages of transfer learning
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18. Scaling Model Training and Serving
Operationalize microservices
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Monitoring and logging with Rust on Google App Engine
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Continuous integration using Rust with GitHub Actions
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Demo: Unit testing Rust
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Demo: GitHub copilot-enabled Rust
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Set up a GCP workstation with Python
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Demo: Google Cloud Shell
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Demo: Google Cloud Editor
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Demo: Google CLI SDK
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Demo: Google gcloud CLI
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Demo: Google App Engine Rust deployment
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Demo: Google App Engine Golang
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19. Conclusion: Developing ML Models
Developing ML models: Next steps
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20. Introduction: Automating and Orchestrating ML Pipelines
Automating and orchestrating ML pipelines: Overview
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Automating and orchestrating ML pipelines: Key terminology
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21. Designing and Implementing Training Pipelines
Prompt engineering for Google BigQuery with ChatGPT 4
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Getting started with Vertex AI
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Understanding TPUs
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TPUs as a technology transition
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Demo: TPU, PyTorch, and MNIST
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22. Implementing Serving Pipelines
TensorFlow serving with a GPU-enabled Docker
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A Rust and PyTorch microservice walkthrough
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Demo: Rust pretrained PyTorch microservice
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23. Conclusion: Automating and Orchestrating ML Pipelines
Automating and orchestrating ML pipelines: Next steps
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24. Introduction: Monitoring, Optimizing, and Maintaining ML Solutions
Monitoring, optimizing, and maintaining ML solutions: Overview
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Monitoring, optimizing, and maintaining ML solutions: Key terminology
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25. ML Solutions
Data drift explained by the naughty child problem
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Load testing with Locust
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Demo: Auditing via logs
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Demo: Logging dashboard
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Demo: Cloud web security scanner
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Demo: Querying logging output with BigQuery
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Demo: Load testing with Rust
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Five whys
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Using Google Courses
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Building a translator with Rust and Hugging Face
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Using PyTorch and Rust for stable diffusion
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Using Rust with PyTorch
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Building a CUDA GPU stress test
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