Getting R into production (Online)

This Master Class will be delivered by Steph Locke

Friendly Title R-Programming
Class TitleGetting R into production
TrainerSteph Locke (UK)
BrochureComing Soon
Delivery Type Online
Delivery Format
  • Total 8 hours
  • Two consecutive days
  • 5 hours each day with one-hour break in between
  • DateComing Soon
    TimeComing Soon

    Price Coming Soon

      * Conversion rate of INR 70 per USD has been taken into consideration
      * For Indian Credit Cards, 18% GST will be charged extra
      * Group discounts available
    Pre-requisitesHave some prior exposure to R, at minimum from reading / training on R basics e.g.
    Key Highlights
  • Fast and effective data manipulation with R and databases
  • Paralellisation and scaling processes
  • Operationalising R code
  • Best-practice package development
  • Takeaways
  • Be able to reduce time to code and the number of lines needed to code data manipulation queries
  • Be able to scale out R processes to leverage additional processing power
  • Understand architectural considerations for deploying R code and be able to implement some key methods
  • Write and manage reusable code for production in your environment
  • Course Outline
  • Querying databases with R
    • Data manipulation with dplyr recap
    • Database connections, standalone and pooled
    • Writing R dbplyr code against database tables
    • Best practices for parameterized code and database writes
  • Scaling R code
    • Scaling and caching core concepts
    • Using the future package to write parallelised code
    • Using furrr to implement parallelisation in data pipelines
    • Reducing repeat requests and implementing caching with the memoise package
    • Using Microsoft ML Server to execute R code remotely
  • Operationalising R
    • Designing an R development environment
    • Designing an R production environment
    • Implementing R in SQL Server fundamentals
    • Developing R APIs with Microsoft and non-Microsoft solutions
    • Deploying R code in Docker containers
  • Reusable R code
    • The R package framework
    • Writing functions and documentation
    • Writing unit tests
    • Managing non-R code dependencies
    • Defensive R programming
  • Querying databases with R
    • Writing dplyr to solve data manipulation tasks that require substantial lines of code in SQL
    • Writing R dbplyr code against database tables with a variety of requirements
  • Scaling R code
    • A paralellised data pipeline
    • Reducing API requests with caching
    • Offloading processing to a remote ML Server
  • Operationalising R
    • Deploying data science models to SQL Server
    • Constructing and deploying an API in a Docker container on Azure Container Instances
  • Reusable R code
    • Building an R package from scratch
    Trainer’s BioSteph is the founder of a consultancy in the UK. Her talks, blog posts, conferences, and business all have one thing in common – they help people get started with data science. Steph holds the Microsoft MVP award for her community contributions. In her spare time, Steph plays board games with her husband and takes copious pictures of her doggos.

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