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• 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)