Source
https://www.udemy.com/course/google-certified-professional-machine-learning-engineer/
Category
File Size
8.6 GB
Publisher
Deepak Dubey
Updated
March 31, 2026
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)
Preview
1 image
