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About the Book
Chapters:
- Introduction to Machine Learning (ML) and Data Science
- Unit Testing and Test Driven Development Methodologies
- Data-intensive APIs Using a RESTful Approach
- Designing and Developing Distributed Systems
- Data Ingress from IoT Devices
- Stream Processing Using Apache Kafka
- Interacting with SQS Topics
- Creating and Maintaining Containerized Application Deployments
- Building and Maintaining a Cloud Based Infrastructure
- Securing Deployments and Cloud-Ops
- Continuous Integration and Continuous Delivery
- Deploying and Maintaining K8s on AWS and GCP
- Google Cloud Dataflow SDK on Apache Beam
- Applying Resource-Oriented Design Concepts
- Creating Machine Learning (ML) Platforms
- Building Analytical Pipelines
- Big Data Algorithms and Data Structures
- Analyzing Data with Python and R
- Application of Quantitative Science to Solve Business Problems
- Deep Data Analysis
- Prometheus, Grafana, and other Application Monitoring Tools
- Developing Kubeflow Solutions in Go
- Where To Go From Here
About the Author
Software Engineer, IT Security Architect with a passion for innovation, and challenges conventional thinking.