Preface (DRAFT)
1 Intro to Data Science and Machine Learning (DRAFT)
- Data Science
- Data Science Modeling
- Machine Learning
- Data Science
- Data Scientists
Data Engineering Handbook
Build durable, scalable, orchestrated software in Go with Kubernetes
Need to collect terabytes of data across all aspects of your operations?
Need to transform complex scientific datasets into innovative software that is deployed across the pipeline, accelerating the pace and quality of all business decisions to unbelievable levels?
Having problems with performance or maintenance?
If so, this book is for you!
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About
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 DeploymentsÂ
- 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
Author
About the Author
Contents
Table of Contents
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