Shreyas Subramanian - AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Exam

1. Chapter 1
2. Chapter 2
3. Chapter 5
4. Chapter 8

1. Chapter 1
2. Chapter 2
3. Chapter 3
4. Chapter 5
5. Chapter 6
6. Chapter 7
7. Chapter 8
8. Chapter 9
9. Chapter 10
10. Chapter 11
11. Chapter 12
12. Chapter 13

Shreyas Subramanian

Stefan Natu

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To our parents.

Although this book bears our names as authors, many other people contributed to its creation. Without their help, this book wouldn't exist, or at best would exist in a lesser form. Kenyon Brown was the acquisitions editor and so helped get the book started. Christine O'Connor, the managing editor, and Caroline Define, project manager, oversaw the book as it progressed through all its stages. Sonam Mishra was the technical editor, who checked the text for technical errors and omissionsbut any mistakes that remain are our own. We would also like to thank Matt Wagner from FreshBooks, who helped connect us with Wiley to write this book. Finally, we would like to thank our wives for their patience as we spent many weekend hours researching the content and writing this book over the past 8 months.

Shreyas Subramanian has a PhD in multilevel systems optimization and application of machine learning to large-scale optimization. He is currently a principal machine learning specialist at Amazon Web Services, and he has worked with several large-scale companies on their business-critical machine learning and optimization problems. Subramanian is passionate about simplifying difficult concepts within optimization, and he holds two patents in areas connected to aviation-related tools and techniques for improving efficiency and security of the airspace. He has also published over 20 conference and journal papers on the topics of aircraft design, evolutionary optimization, distributed optimization, and multilevel systems or systems optimization. He has several years of experience building machine learning and optimization models for customers in large enterprises to small startups, while taking part in and winning hackathons on the side. Subramanian is passionate about teaching practical machine learning to citizen data scientists and has trained hundreds of customers in private, hands-on environments and has helped customers build proofs-of-concept that are now in production today, providing millions of dollars worth of revenue to the AWS business as well as customers.

Stefan Natu is a principal machine learning (ML) architect at Alexa AI, where he is building an ML platform for Alexa scientists and engineers. Prior to that, Natu was the lead ML architect at Amazon Web Services, where he focused on financial services and helped major investment banking, asset management, and insurance customers build and operationalize ML use cases on AWS, with an emphasis on security, enterprise data, and model governance. Natu has developed and evangelized common ML architecture and infrastructure patterns globally across AWS highly regulated customers, leading to numerous production ML deployments and millions of dollars in AWS cloud revenue. He has authored over 25 AWS machine learning blogs, code samples. and whitepapers, and is a frequent speaker at conferences such as AWS re:Invent. He completed his PhD in atomic and condensed matter physics from Cornell University, and he worked as a research physicist at ExxonMobil, submitting two patents and over 25 peer-reviewed publications. Natu is passionate about mentorship and has served as a technical adviser at Insight Data Science, where he guided students in their transition from careers in academia to industry.