Transform business challenges into machine learning (ML) use cases and select the most suitable solution (ML vs non-ML, custom vs pre-packaged). Define how the model output can solve the business problem effectively.

Identify the data sources, both available and ideal, for the ML solution. Determine the specific ML problems, including the problem type, desired prediction outcomes, and input/output formats. Establish business success criteria that align with ML metrics and key results.

Evaluate the risks associated with ML solutions by assessing their impact on the business, readiness of the ML solution, and data readiness. Design reliable, scalable, and available ML solutions by choosing appropriate ML services and components.

Develop strategies for data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving. Consider different hardware options (CPU, GPU, TPU, edge devices) provided by Google Cloud.

Design architectures that adhere to security requirements across various sectors. Explore data through visualization, statistical fundamentals, data quality assessment, and handling data constraints.

Build efficient data pipelines to organize and optimize datasets, handle missing data and outliers, and prevent data leakage. Create input features, ensuring consistency in data pre-processing, encoding structured data, managing feature selection, handling class imbalance, and applying transformations.

Construct models by selecting the appropriate framework, considering interpretability, transfer learning, data augmentation, and managing overfitting/underfitting. Train models using various file types, manage training environments, tune hyperparameters, and track training metrics.

Test models through unit tests, performance comparisons, and utilizing Vertex AI for model explainability. Scale model training and serving by distributing training and scaling prediction services.

Design and implement training pipelines by identifying components, managing orchestration frameworks, considering hybrid or multicloud strategies, and utilizing TFX components. Implement serving pipelines, manage serving options, conduct performance tests, and configure schedules.

Track and audit metadata by organizing and tracking experiments, managing model and dataset versioning, and understanding model and dataset lineage.

Monitor and troubleshoot ML solutions by measuring performance, implementing logging strategies, and establishing continuous evaluation metrics. Fine-tune performance for training and serving in production by optimizing input pipelines and utilizing simplification techniques.