Google Cloud Machine Learning Engineer
Resources
- Google Cloud Skills Boost: Machine Learning Engineer Learning Path
- Google ML education
- Professional MLE study guide
Objectives
- architecting low-code AI solutions
- develop ML models with BQML (C1M2, C2L1, C4 all, C5 all, C6M3, C16M3)
- build AI solutions by using ML APIs or foundational models (C1M3, C2L7-9, C16L2, C17)
- training models by using AutoML (C1 all, C3)
- collaborating within and across teams to manage data and models
- explore and process data (C1M2-4, C3, C5 all, C10)
- model prototyping using Jupyter notebooks (C3)
- tracking and running ML experiments (C16L1, C14)
- scaling prototypes into ML models
- building models (C8)
- training models (C1M4, C12, C13, C8M3-6, C16L3)
- choosing the right hardware (C8M3)
- serving and scaling models
- serving models (C6M1, C8M2, C16L3)
- scaling online model serving (C6M1, )
- automating and orchestrating ML pipelines
- developing end-to-end ML pipelines (C1M4, C8, C9, C16L4)
- automate model retraining (C16, C15)
- monitoring AI solutions
- identifying risks to ML solutions (C8M1,2,6, C18, C19, C20)
- monitoring, testing, troubleshooting ML solutions (C16L1,L3,L5, C8M2,M6, C7M4)
Curriculum
| ID | Chapter | Topic | Resources |
|---|---|---|---|
| C1M1 | Introduction to AI and Machine Learning on Google Cloud | AI Foundations on GC | Notes |
| C1M2 | AI development on GC | ||
| C1M3 | ML Workflow and Vertex AI | ||
| C1M4 | GenAI on GC | ||
| C2L1 | Prepare data for ML APIs on GC | Vertex AI | |
| C2L2 | Dataprep | ||
| C2L3 | Dataflow | ||
| C2L4 | Dataflow | ||
| C2L5 | Dataproc | ||
| C2L6 | Dataproc | ||
| C2L7 | Cloud NL API | ||
| C2L8 | Speech-to-text API | ||
| C2L9 | Video intelligence | ||
| C2L10 | Prepare data for ML API on GC | ||
| C3 | Working with Notebooks in Vertex AI | Working with Notebooks in Vertex AI | |
| C4L1 | Create ML Models with BigQuery ML | Getting started with BigQuery ML | |
| C4L2 | Predict visitor purchases with classification with BQML | ||
| C4L3 | Predict taxi fare with BQML | ||
| C4L4 | Bracktology with Google ML | ||
| C4L5 | Create ML models with BQML | ||
| C5L1 | Engineer Data for Predictive Modeling with BigQuery ML | Create a data transformation pipeline with cloud dataprep | |
| C5L2 | ETL processing | ||
| C5L3 | Predict visitor purchases | ||
| C5L4 | Engineer data for predicitive modeling | ||
| C6M1 | Feature engineering | Intro | |
| C6M2 | Raw data to features | ||
| C6M3 | Feature engineering | ||
| C6M4 | Preprocessing and feature creation | ||
| C6M5 | Feature crosses | ||
| C6M6 | Introduction to tensorflow transformation | ||
| C7L1 | Tensorflow on GC | TensorFlow ecosystem | |
| C7L2 | Design and build an input data pipeline | ||
| C7L3 | Building Neural networks with TF and Keras | ||
| C7L4 | Training at scale with Vertex AI | ||
| C8 | Production ML systems | Less relevant | |
| C9 | MLOps: getting started | Less relevant | |
| C10 | MLOps with vertex AI: manage features | Less relevant | |
| C11, C13 | Intro to Generative AI, MLOps | ||
| C12 | Intro to LLM | ||
| C14 | MLOps with vertex AI: model evaluation | Less relevant | |
| C15 | ML Pipelines on GC | Less relevant | |
| C16L1 | Build and Deploy Machine Learning Solutions on Vertex AI | Vertex AI | |
| C16L1 | Identify damaged car parts with vertex AutoML vision | ||
| C16L1 | Deploy a BQML customer churn classifier | ||
| C16L1 | Vertex pipelines | ||
| C16L5 | Build and deploy ML solutions with vertex AI | ||
| C17 | Build GenAI apps on GC | ||
| C18 | Responsible AI: fairness and bias | ||
| C19 | Responsible AI: interpretability and transparency | ||
| C20 | Responsible AI: privacy and safety |
Other topics
| Title |
|---|
| Cloud services overview |
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