Machine Learning Infrastructure and Best Practices for Software Engineers

Table of Contents

1. Machine Learning Compared to Traditional Software
2. Elements of a Machine Learning Software System
3. Data in Software Systems – Text, Images, Code, Features
4. Data Acquisition, Data Quality and Noise
5. Quantifying and Improving Data Properties
6. Types of Data in ML Systems
7. Feature Engineering for Numerical and Image Data
8. Feature Engineering for Natural Language Data
9. Types of Machine Learning Systems – Feature-Based and Raw Data Based (Deep Learning)
10. Training and evaluation of classical ML systems and neural networks
11. Training and evaluation of advanced algorithms – deep learning, autoencoders, GPT-3
12. Designing machine learning pipelines (MLOps) and their testing
13. Designing and implementation of large scale, robust ML software – a comprehensive example
14. Ethics in data acquisition and management
15. Ethics in machine learning systems
16. Integration of ML systems in ecosystems
17. Summary and where to go next