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About the Book

Chapters:

  1. Introduction to Machine Learning (ML) and Data Science
  2. Unit Testing and Test Driven Development Methodologies
  3. Data-intensive APIs Using a RESTful Approach
  4. Designing and Developing Distributed Systems
  5. Data Ingress from IoT Devices
  6. Stream Processing Using Apache Kafka
  7. Interacting with SQS Topics
  8. Creating and Maintaining Containerized Application Deployments 
  9. Building and Maintaining a Cloud Based Infrastructure
  10. Securing Deployments and Cloud-Ops
  11. Continuous Integration and Continuous Delivery
  12. Deploying and Maintaining K8s on AWS and GCP
  13. Google Cloud Dataflow SDK on Apache Beam
  14. Applying Resource-Oriented Design Concepts
  15. Creating Machine Learning (ML) Platforms
  16. Building Analytical Pipelines
  17. Big Data Algorithms and Data Structures
  18. Analyzing Data with Python and R
  19. Application of Quantitative Science to Solve Business Problems
  20. Deep Data Analysis
  21. Prometheus, Grafana, and other Application Monitoring Tools
  22. Developing Kubeflow Solutions in Go
  23. Where To Go From Here

About the Author

Lex Sheehan’s avatar Lex Sheehan

@lex_sheehan

Software Engineer, IT Security Architect with a passion for innovation, and challenges conventional thinking.

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