Google Certified Professional Machine Learning Engineer

Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations
(18) Ratings
4,976 students
Created by Deepak Dubey

What you'll learn

  • Framing ML problems
  • Architecting ML solutions
  • Designing data preparation and processing systems
  • Developing ML models
  • Automating and orchestrating ML pipelines
  • Monitoring, optimizing, and maintaining ML solutions
This course includes:
16.5 total hours on-demand video
0 articles
13 downloadable resources
64 lessons
Full lifetime access
Access on mobile and TV
Certificate of completion

Course content


  • 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.


  • 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

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