GPM learning engi ass GCP | firstconsulting-1

• 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)

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