A concise, technical guide focused on best practices and design patterns for building and deploying efficient machine learning models in Python using TensorFlow 2. It serves as a practical, easy-to-use reference for ML engineers and data scientists to make informed decisions for various real-world use cases. This easy-to-use reference addresses TensorFlow 2 design patterns in Python. It provides code snippets as templates to help machine learning engineers and data scientists build, train, and deploy models efficiently in their daily workflows.
Specifications
Key Features
Design Patterns: Explores best practices for TensorFlow model patterns and ML workflows to ensure efficiency.
Code Snippets: Includes ready-to-use code templates for building and deploying various machine learning models.
Decision Support: Helps readers make informed architectural choices for different data science use cases.
Data Preprocessing: Offers detailed guidance on preparing tabular, text, and image data for training.
Pocket-Sized Reference: Designed as a quick, portable guide for developers to reference while working on active projects.