Google Cloud Professional Machine Learning Engineer

Published weight ~13%. Developing ML models with BigQuery ML or AutoML, and building AI solutions using Google Cloud AI APIs and foundational models (Model Garden, Gemini, industry APIs).

• Develop ML models using BigQuery ML or AutoML
• Build AI solutions using Google Cloud AI APIs or foundational models

Published weight ~16%. Exploring and preprocessing data for ML, model prototyping in notebooks (Workbench, Colab Enterprise), and tracking and running ML experiments.

• Explore and preprocess data for ML
• Model prototyping using notebooks
• Track and run ML experiments

Published weight ~21%. Building models given cost/complexity/latency/scalability, training models (SDKs, ingestion, hyperparameter tuning, fine-tuning), and choosing appropriate training hardware (CPU/GPU/TPU, distributed training).

• Build models given the task (cost, complexity, latency, scalability)
• Train models
• Choose appropriate hardware for training

Published weight ~20%. Serving models for batch and online inference (containers, Model Registry, rollout strategies) and scaling online model serving (Feature Store, endpoints, hardware, throughput).

• Serve models (batch and online inference)
• Scale online model serving

Published weight ~18%. Developing end-to-end ML pipelines (validation, orchestration, training/serving consistency) and automating model retraining (retraining policy, CI/CD/CT pipelines).

• Develop end-to-end ML pipelines
• Automate model retraining

Published weight ~13%. Identifying risks to AI solutions (security, responsible AI, explainability) and monitoring, testing, and troubleshooting AI solutions (Model Monitoring, drift/skew, gen AI evaluation).

• Identify risks to AI solutions (security, responsible AI, explainability)
• Monitor, test, and troubleshoot AI solutions