Operations
28
Best practices for version control
R in Production
Welcome
1
Introduction
2
The whole game
3
Living a project-oriented life
Characteristtics of code in production
4
Running code on another machine
5
Right tool for the job
6
Authentication and authorization
7
Optimizing for scalability and efficiency
8
Logging
9
Error handling and debugging
Getting code to production
10
Cross-platform paper cuts
11
GitHub actions
12
Package installation
Running code multiple times
13
Run code repeatedly and predictably
14
Schema changes
15
Packages and platform
16
Dealing with a changing universe
17
Handling changing requirements
Working with data
18
Accessing data
19
Managing credentials and permissions
20
Integrate with external tools and systems
Managing packages and environments
21
Package management best practices
22
Reproducibility across environments
23
Collaborating with IT and Security teams
Security
24
Security in code
25
Security in packages
26
Security in deployment
27
Processes to respond to a security event
Operations
28
Best practices for version control
29
CI/CD for data science projects
30
Ship it, don’t sink it
31
Care and feeding
32
Choosing infrastructure and architecture for R projects
Shared responsibility
33
Building up team conventions
34
A more detailed discussion on internal packages
35
Parquet
36
Advice for working with IT teams
37
Masiello Metric ;)
Operations
28
Best practices for version control
28
Best practices for version control
Using Git for version control of R scripts, data, and models
Branching strategies for development, testing, and production environments
Operations
29
CI/CD for data science projects