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https://github.com/gperdrizet/gcsb_mle

Google Cloud Skills Boost Machine Learning Engineer Learning Path
https://github.com/gperdrizet/gcsb_mle

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Google Cloud Skills Boost Machine Learning Engineer Learning Path

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README 

        
# GCSB_MLE



This repository will be used to track and document my progress through the [Google Cloud Skills Boost Machine Learning Engineer Learning Path](https://www.cloudskillsboost.google/paths/17). Each course in the learning path listed below is associated with an issue and a GitHub project is used to track overall progress. Work for each section is completed on a branch which is merged and closed upon completion.



**Note:** The section numbering below follows that given in the [study guide](https://github.com/gperdrizet/GCSB_MLE/blob/main/course_introduction_materials/machine_learning_engineer_study_guide.pdf) where the first two introductory sections listed on the [learning path page](https://www.cloudskillsboost.google/paths/17) are not included in the numbering.



## Learning path outline



### [Course 01. Introduction to AI and Machine Learning on Google Cloud (8 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/593)



- ~~**Module 1**: AI Foundations on Google Cloud~~

- ~~**Module 2**: AI Development on Google Cloud~~

- ~~**Module 3**: ML Workflow and Vertex AI~~

- ~~**Module 4**: Generative AI on Google Cloud~~



### [Course 02. Prepare Data for ML APIs on Google Cloud (6.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/631)



- ~~**Lab 1**: Vertex AI: Qwik Start~~

- ~~**Lab 2**: Dataprep: Qwik Start~~

- ~~**Lab 3**: Dataflow: Qwik Start - Templates~~

- ~~**Lab 4**: Dataflow: Qwik Start - Python~~

- ~~**Lab 5**: Dataproc: Qwik Start - Console~~

- ~~**Lab 6**: Dataproc: Qwik Start - Command Line~~

- ~~**Lab 7**: Cloud Natural Language API: Qwik Start~~

- ~~**Lab 8**: Speech-to-Text API: Qwik Start~~

- ~~**Lab 9**: Video Intelligence: Qwik Start~~

- ~~**Lab 10**: Prepare Data for ML APIs on Google Cloud: Challenge Lab~~



### [Course 03. Working with Notebooks in Vertex AI (0.75 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/923)



**Mini-course**: 8 lessons



- ~~**Lesson 1**: Working with Notebooks in Vertex AI~~

- ~~**Lesson 2**: Vertex AI Notebook Solutions~~

- ~~**Lesson 3**: Vertex AI Colab Enterprise notebooks~~

- ~~**Lesson 4**: Vertex AI Workbench instance notebooks~~

- ~~**Summary**~~

- ~~**Quiz**: Working with Notebooks in Vertex AI~~

- ~~**Lab 1**: Exploratory Data Analysis using Bigquery and Colab Enterprise (2 hrs)~~

- ~~**Lab 2**: Exploratory Data Analysis using Bigquery and Workbench Instances (2 hrs)~~



### [Course 04. Create ML Models with BigQuery ML (5.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/626)



- **Lab 1**: ~~Getting Started with BigQuery ML~~

- **Lab 2**: ~~Predict Visitor Purchases with a Classification Model in BigQuery ML~~

- **Lab 3**: ~~Predict Taxi Fare with a BigQuery ML Forecasting Model~~

- **Lab 4**: ~~Bracketology with Google Machine Learning~~

- **Lab 5**: ~~Create ML Models with BigQuery ML: Challenge Lab~~



### [Course 05. Engineer Data for Predictive Modeling with BigQuery ML (4.25 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/627)



- **Lab 1**: Creating a Data Transformation Pipeline with Cloud Dataprep

- **Lab 2**: ETL Processing on Google Cloud Using Dataflow and BigQuery (Python)

- **Lab 3**: Predict Visitor Purchases with a Classification Model in BigQuery ML

- **Lab 4**: Engineer Data for Predictive Modeling with BigQuery ML: Challenge Lab



### [Course 06. Feature Engineering (24 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/11)



- **Module 1**: Introduction to Vertex AI Feature Store

- **Module 2**: Raw Data to Features

- **Module 3**: Feature Engineering

- **Module 4**: Preprocessing and Feature Creation

- **Module 5**: Feature Crosses: TensorFlow Playground

- **Module 6**: Introduction to TensorFlow Transform



### [Course 07. Build, Train and Deploy ML Models with Keras on Google Cloud (15.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/12)



- **Module 1**: Introduction to the TensorFlow Ecosystem

- **Module 2**: Design and Build an Input Data Pipeline

- **Module 3**: Building Neural Networks with the TensorFlow and Keras API

- **Module 4**: Training at Scale with Vertex AI



### [Course 08. Production Machine Learning Systems (16 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/17)



- **Module 1**: Architecting Production ML System

- **Module 2**: Designing Adaptable ML System Designing High-Performance ML Systems

- **Module 3**: Designing High-Performance ML Systems

- **Module 4**: Hybrid ML Systems

- **Module 5**: Troubleshooting ML Production Systems



### [Course 09. Machine Learning Operations (MLOps): Getting Started (8 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/158)



- **Module 1**: Employing Machine Learning Operations

- **Module 2**: Vertex AI and MLOps on Vertex AI



### [Course 10. Machine Learning Operations (MLOps) with Vertex AI: Manage Features (8 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/584)



- **Module 1**: Introduction to Vertex AI Feature Store

- **Module 2**: An In-Depth Look



### [Course 11. Introduction to Generative AI (0.75 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/536)



- **Mini-course**: 1 lesson



### [Course 12. Introduction to Large Language Models (0.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/539)



- **Mini-course**: 1 lesson



### [Course 13. Machine Learning Operations (MLOps) for Generative AI (0.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/927)



- **Mini Course**: 5 lessons



### [Course 14. Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation (2.5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/1080)



- **Module 1**: Introduction to Model Evaluation

- **Module 2**: Model Evaluation for Generative AI



### [Course 15. ML Pipelines on Google Cloud (2.25 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/191)



- **Module 1**: Introduction to TFX Pipelines

- **Module 2**: Pipeline Orchestration with TFX

- **Module 3**: Custom Components and CI/CD for TFX Pipelines

- **Module 4**: ML Metadata with TFX

- **Module 5**: Continuous Training with Multiple SDKs, KubeFlow & AI Platform Pipelines

- **Module 6**: Continuous Training with Cloud Composer

- **Module 7**: ML Pipelines with MLflow



### [Course 16. Build and Deploy Machine Learning Solutions on Vertex AI (8.25 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/684)



- **Lab 1**: Vertex AI: Qwik Start

- **Lab 2**: Identify Damaged Car Parts with Vertex AutoML Vision

- **Lab 3**: Deploy a BigQuery ML Customer Churn Classifier to Vertex AI for Online Predictions

- **Lab 4**: Vertex Pipelines: Qwik Start

- **Lab 5**: Build and Deploy Machine Learning Solutions with Vertex AI: Challenge Lab



### [Course 17. Create Generative AI Applications on Google Cloud (4 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/1120)



- **Module 1**: Generative AI Applications

- **Module 2**: Prompts

- **Module 3**: Retrieval Augmented Generation (RAG)



### [Course 18. Responsible AI for Developers: Fairness and Bias (4 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/985)



- **Module 1**: AI Interpretability and Transparency

- **Module 2**: Modernizing Infrastructure in the Cloud



### [Course 19. Responsible AI for Developers: Interpretability and Transparency (3 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/989)



- **Module 1**: AI Interpretability and Transparency

- **Module 2**: Modernizing Infrastructure in the Cloud



### [Course 20. Responsible AI for Developers: Privacy and Safety (5 hours)](https://www.cloudskillsboost.google/paths/17/course_templates/1036)



- **Module 1**: AI Privacy

- **Module 2**: AI Safety