Summary
To summarize, we have covered different types of ML (supervised and unsupervised) and explored how to save and execute models in various formats. Additionally, we delved into the different phases of ML (data extraction, data preparation, model development, model deployment, and inferencing) and discussed the privacy threats and attacks associated with each phase in detail.
In the next chapter, we will dive deeper into privacy-preserving data analysis and focus on understanding the concept of differential privacy. This will allow us to explore techniques and methodologies that ensure privacy while conducting data analysis tasks. By gaining a thorough understanding of differential privacy, we can better safeguard sensitive information and mitigate privacy risks in the context of ML.
