Description

• 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)