Google Professional Machine Learning Engineer Associate GCP

Section 1: Framing ML problems

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)

 Defining ML problems. Considerations include:

• Problem type (e.g., classification, regression, clustering)

• Outcome of model predictions

• Input (features) and predicted output format

 Defining business success criteria. Considerations include:

• Alignment of ML success metrics to the business problem

• Key results

• Determining when a model is deemed unsuccessful

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)
  
   

Section 2: Architecting ML solutions

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
   

Choosing appropriate Google Cloud hardware components. Considerations include:

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

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])
  
   

Section 3: Designing data preparation and processing systems

Exploring data (EDA). Considerations include:

• Statistical fundamentals at scale

• Evaluation of data quality and feasibility

• Establishing data constraints (e.g., TFDV)
   

Building data pipelines. Considerations include:

• Organizing and optimizing training datasets

• Data validation

• Handling missing data

• Handling outliers

• Data leakage
   

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)

Section 4: Developing ML models

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
   

 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
   

 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
   

 Scaling model training and serving. Considerations include:

• Distributed training

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

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Section 5: Automating and orchestrating ML pipelines

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 multicloud strategies

• System design with TFX components/Kubeflow DSL
   

 Implementing serving pipelines. Considerations include:

• Serving (online, batch, caching)

• Google Cloud serving options

• Testing for target performance

• Configuring trigger and pipeline schedules
   

 Tracking and auditing metadata. Considerations include:

• Organizing and tracking experiments and pipeline runs

• Hooking into model and dataset versioning

• Model/dataset lineage
  
   

Section 6: Monitoring, optimizing, and maintaining ML solutions

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
   

Tuning performance of ML solutions for training and serving in production. Considerations include:

• Optimization and simplification of input pipeline for training

• Simplification techniques