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:

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

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

• Assessing and communicating business impact
• Assessing ML solution readiness
• Assessing data readiness and potential limitations
• 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:

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

3.2 Building data pipelines. Considerations include:

• Organizing and optimizing training datasets
• Data validation
• Handling missing data
• Handling outliers
• Data leakage

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

• Ensuring consistent data pre-processing between training and serving
• Encoding structured data types
• Feature selection
• Class imbalance
• Feature crosses
• 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 AI Platform

4.4 Scaling model training and serving. Considerations include:

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

5.1 Designing and implementing training pipelines. Considerations include:

• Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
• Orchestration framework (e.g., Kubeflow Pipelines/AI Platform Pipelines, Cloud Composer/Apache Airflow)
• Hybrid or multi-cloud strategies
• System design with TFX components/Kubeflow DSL

5.2 Implementing serving pipelines. Considerations include:

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

5.3 Tracking and auditing metadata. Considerations include:

• Organizing and tracking experiments and pipeline runs
• Hooking into model and dataset versioning
• 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