Google Cloud Professional Machine Learning EngineerStudy guide

Understand choosing the right BigQuery ML model by business problem (linear/binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders), feature engineering and prediction with BigQuery ML, and AutoML (data prep/labeling/Tabular Workflows, custom/forecasting models on tabular data, configuring/debugging trained models). (Note: legacy AutoML Text is being deprecated in favor of Gemini-based models on Vertex AI; AutoML for image/video/tabular continues.)

Understand building applications with ML APIs from Model Garden, using industry-specific APIs (Document AI API, Retail API, etc.), and implementing retrieval augmented generation (RAG) applications with Vertex AI Agent Builder.

Understand exploring organization-wide data (Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop), organizing data types (tabular/text/speech/image/video), managing datasets in Vertex AI, preprocessing (Dataflow, TensorFlow Extended [TFX], BigQuery), creating/consolidating features in Vertex AI Feature Store, privacy of data usage (PII/PHI), and ingesting data into Vertex AI for inference.

Understand choosing the Jupyter backend on Google Cloud (Vertex AI Workbench, Colab Enterprise, notebooks on Dataproc), security best practices in Vertex AI Workbench, Spark kernels, code-repository integration, developing with frameworks (TensorFlow/PyTorch/sklearn/Spark/JAX), leveraging foundation/open-source models in Model Garden, tracking ML experiments (Vertex AI Experiments, Kubeflow Pipelines, Vertex AI TensorBoard), and evaluating generative AI solutions.

Understand choosing ML framework and model architecture, modeling techniques per interpretability requirements, organizing training data (tabular/text/speech/image/video), ingesting file types (CSV/JSON/images/Hadoop/DB), training with SDKs (Vertex AI custom training, Kubeflow on GKE, AutoML, Tabular Workflows), reliable distributed training, hyperparameter tuning, troubleshooting training failures, and fine-tuning foundation models.

Understand evaluating compute and accelerator options (CPU, GPU, TPU, edge devices) and distributed training with TPUs and GPUs (Reduction Server on Vertex AI, Horovod).

Understand batch and online inference (Vertex AI, Dataflow, BigQuery ML, Dataproc), serving with frameworks (PyTorch, XGBoost, etc.), organizing a model registry, and A/B testing model versions.

Understand Vertex AI Feature Store, Vertex AI public and private endpoints, choosing appropriate hardware (CPU/GPU/TPU/edge), scaling the serving backend by throughput (Vertex AI Prediction, containerized serving), and tuning models for training/serving in production (simplification, optimizing performance/latency/memory/throughput).

Understand data and model validation, consistent preprocessing between training and serving, hosting third-party pipelines (MLFlow), identifying components/parameters/triggers/compute (Cloud Build, Cloud Run), orchestration frameworks (Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer), hybrid/multicloud strategies, and system design with TFX components or Kubeflow DSL.

Understand determining an appropriate retraining policy, CI/CD model deployment (Cloud Build, Jenkins), tracking/comparing model artifacts and versions (Vertex AI Experiments, Vertex ML Metadata), hooking into model/dataset versioning, and model and data lineage.

Understand building secure AI systems by protecting against unintentional exploitation of data/models (hacking), aligning with Google's Responsible AI practices (monitoring for bias), assessing AI solution readiness (fairness, bias), and model explainability on Vertex AI (Explainable AI).

Understand establishing continuous evaluation metrics (Vertex AI Model Monitoring, Explainable AI), monitoring for training-serving skew, monitoring for feature attribution drift, monitoring model performance against baselines/simpler models/over time, and monitoring for common training and serving errors.