Google Professional-Machine-Learning-Engineer Exam Syllabus

1.1 Translating business challenges into ML use cases. Considerations include:

• Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements
• Defining how the model output should be used to solve the business problem
• Deciding how incorrect results should be handled
• Identifying data sources (available vs. ideal)

1.2 Defining ML problems. Considerations include:

• Problem type (e.g., classification, regression, clustering)
• Outcome of model predictions
• Input (features) and predicted output format

1.3 Defining business success criteria. Considerations include:

• a. Alignment of ML success metrics to the business problem
• b. Key results
• c. Determining when a model is deemed unsuccessful

1.4 Identifying risks to feasibility of ML solutions. Considerations include:

• a. Assessing and communicating business impact
• b. Assessing ML solution readiness
• c. Assessing data readiness and potential limitations
• d. Aligning with Google’s Responsible AI practices (e.g., different biases)

2.1 Designing reliable, scalable, and highly available ML solutions. Considerations include:

• Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)
• Component types (e.g., data collection, data management)
• Exploration/analysis
• Feature engineering
• Logging/management
• Automation
• Orchestration
• Monitoring
• Serving

2.2 Choosing appropriate Google Cloud hardware components. Considerations include:

• Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)

2.3 Designing architecture that complies with security concerns across sectors/industries.

Considerations include:

• Building secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking)
• Privacy implications of data usage and/or collection (e.g., handling sensitive data such as Personally Identifiable Information [PII] and Protected Health Information [PHI])

3.1 Exploring data (EDA). Considerations include:

• a. Visualization
• b. Statistical fundamentals at scale
• c. Evaluation of data quality and feasibility
• d. Establishing data constraints (e.g., TFDV)

3.2 Building data pipelines. Considerations include:

• a. Organizing and optimizing training datasets
• b. Data validation
• c. Handling missing data
• d. Handling outliers
• e. Data leakage

3.3 Creating input features (feature engineering). Considerations include:

• a. Ensuring consistent data pre-processing between training and serving
• b. Encoding structured data types
• c. Feature selection
• d. Class imbalance
• e. Feature crosses
• f. Transformations (TensorFlow Transform)

4.1 Building models. Considerations include:

• Choice of framework and model
• Modeling techniques given interpretability requirements
• Transfer learning
• Data augmentation
• Semi-supervised learning
• Model generalization and strategies to handle overfitting and underfitting

4.2 Training models. Considerations include:

• Ingestion of various file types into training (e.g., CSV, JSON, IMG, parquet or databases, Hadoop/Spark)
• Training a model as a job in different environments
• Hyperparameter tuning
• Tracking metrics during training
• Retraining/redeployment evaluation

4.3 Testing models. Considerations include:

• Unit tests for model training and serving
• Model performance against baselines, simpler models, and across the time dimension
• Model explainability on Vertex AI

4.4 Scaling model training and serving. Considerations include:

• Distributed training
• Scaling prediction service (e.g., Vertex AI Prediction, containerized serving)

5.1 Designing and implementing training pipelines. Considerations include:

• a. Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
• b. Orchestration framework (e.g., Kubeflow Pipelines/Vertex AI Pipelines, Cloud Composer/Apache Airflow)
• c. Hybrid or multicloud strategies
• d. System design with TFX components/Kubeflow DSL

5.2 Implementing serving pipelines. Considerations include:

• a. Serving (online, batch, caching)
• b. Google Cloud serving options
• c. Testing for target performance
• d. Configuring trigger and pipeline schedules

5.3 Tracking and auditing metadata. Considerations include:

• a. Organizing and tracking experiments and pipeline runs
• b. Hooking into model and dataset versioning
• c. Model/dataset lineage

6.1 Monitoring and troubleshooting ML solutions. Considerations include:

• Performance and business quality of ML model predictions
• Logging strategies
• Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)
• Understanding Google Cloud permissions model
• Identification of appropriate retraining policy
• Common training and serving errors (TensorFlow)
• ML model failure and resulting biases

6.2 Tuning performance of ML solutions for training and serving in production.

Considerations include:

• Optimization and simplification of input pipeline for training
• Simplification techniques