Google Certified Professional Machine Learning Engineer

Google Certified Professional Machine Learning Engineer


  • Translate business challenges into ML use cases
  • Choose the optimal solution (ML vs non-ML, custom vs pre-packaged)
  • Define how the model output should solve the business problem
  • Identify data sources (available vs ideal)
  • Define ML problems (problem type, outcome of predictions, input and output formats)
  • Define business success criteria (alignment of ML metrics, key results)
  • Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)
  • Design reliable, scalable, and available ML solutions
  • Choose appropriate ML services and components
  • Design data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategies
  • Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)
  • Design architectures that comply with security concerns across sectors
  • Explore data (visualization, statistical fundamentals, data quality, data constraints)
  • Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)
  • Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)
  • Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)
  • Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)
  • Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)
  • Scale model training and serving (distribute training, scale prediction service)
  • Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)
  • Implement serving pipelines (manage serving options, test for target performance, configure schedules)
  • Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)
  • Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)
  • Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)

Who this course is for:

  • Anyone wishing to get Google Cloud Certified Professional Machine Learning Engineer


  • Some prior experience with Google Cloud and Machine Learning will help. Also if you are already certified with Google Professional Data Engineer that will help you greatly.

Last Updated 7/2023

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Google Certified Professional Machine Learning (6.5 GB) | Mirror

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Google Certified Professional Machine Learning Engineer.torrent (155 KB) | Mirror

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