MLOps and Admin

Outgrowing your laptop with R and Positron

R-Ladies Abuja has posted a recording of a recent talk on Positron, and you can find it here!

Julia Silge

News from the sparkly-verse

Highlights to the most recent updates to `sparklyr` and friends

Edgar Ruiz

Announcing bundle

Some models store information in locations that cannot be saved via regular R serialization. The new bundle package provides a consistent interface for bundling model objects with their references so that they can be safely saved and re-loaded in production

Julia Silge

Announcing vetiver for MLOps in R and Python

We are thrilled to announce the release of vetiver, a framework for MLOps tasks in R and Python. Use vetiver to version, share, deploy, and monitor a trained model

Julia Silge

Integrating Dynamic R and Python Models in Tableau Using plumbertableau

The plumbertableau and fastapitableau packages allow you to integrate dynamic R and Python models in Tableau, enhancing your organization’s preferred dashboard tool

Sharing Data With the pins Package

The pins package publishes R objects, such as data sets and predictive models, on a virtual corkboard so that you can share and reuse them easily and safely

pins 1.0.0

pins 1.0.0 is now available on CRAN! The pins package publishes data, models, and other R objects, making it easy to share them across projects and with your colleagues. Pins can be versioned, making it straightforward to track changes, re-run analyses on historical data, and undo mistakes

Hadley Wickham

sparklyr.sedona: A sparklyr extension for analyzing geospatial data

We are excited to announce the availability of sparklyr.sedona, a sparklyr extension making geospatial functionalities of the Apache Sedona library easily accessible from R

sparklyr 1.7: New data sources and spark_apply() capabilities, better interfaces for sparklyr extensions, and more!

Sparklyr 1.7 delivers much-anticipated improvements, including R interfaces for image and binary data sources, several new spark_apply() capabilities, and better integration with sparklyr extensions

sparklyr 1.6: weighted quantile summaries, power iteration clustering, spark_write_rds(), and more

The sparklyr 1.6 release introduces weighted quantile summaries, an R interface to power iteration clustering, spark_write_rds(), as well as a number of dplyr-related improvements

sparklyr 1.5: better dplyr interface, more sdf_* functions, and RDS-based serialization routines

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 placed much more emphasis on enhancing existing sparklyr features rather than creating new ones. As a result, many valuable suggestions from sparklyr users were taken into account and were successfully addressed in a long list of bug fixes and improvements

sparklyr 1.4: Weighted Sampling, Tidyr Verbs, Robust Scaler, RAPIDS, and more

Sparklyr 1.4 is now available! This release comes with delightful new features such as weighted sampling and tidyr verbs support for Spark dataframes, robust scaler for standardizing data based on median and interquartile range, spark_connect interface for RAPIDS GPU acceleration plugin, as well as a number of dplyr-related improvements

Training ImageNet with R

This post explores how to train large datasets with TensorFlow and R. Specifically, we present how to download and repartition ImageNet, followed by training ImageNet across multiple GPUs in distributed environments using TensorFlow and Apache Spark

Parallelized sampling using exponential variates

How can the seemingly iterative process of weighted sampling without replacement be transformed into something highly parallelizable? Turns out a well-known technique based on exponential variates accomplishes exactly that

sparklyr 1.3: Higher-order Functions, Avro and Custom Serializers

Sparklyr 1.3 is now available, featuring exciting new functionalities such as integration of Spark higher-order functions and data import/export in Avro and in user-defined serialization formats

pins 0.4.0: Versioning

A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and protects your code from breaking when remote resources change unexpectedly

sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect

sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect

A new sparklyr release is now available. This sparklyr 1.2 release features new functionalities such as support for Databricks Connect, a Spark backend for the 'foreach' package, inter-op improvements for working with Spark 3.0 preview, as well as a number of bug fixes and improvements addressing user-visible pain points

pins 0.4: Versioning

A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and protects your code from breaking when remote resources change unexpectedly

sparklyr 1.1: Foundations, Books, Lakes and Barriers

pins 0.3.0: Azure, GCloud and S3

New CRAN release for pins adds support for cloud boards (Azure, Google Cloud and S3), new functions, use cases and many other improvements

pins: Pin, Discover and Share Resources

RStudio Team

rstudio::conf 2019 Workshop materials now available

Mine Çetinkaya-Rundel

sparklyr 0.9: Streams and Kubernetes

sparklyr 0.8: Production pipelines and graphs

GPU Workstations in the Cloud with Paperspace

If you don't have local access to a modern NVIDIA GPU, your best bet is typically to run GPU intensive training jobs in the cloud. Paperspace is a cloud service that provides access to a fully preconfigured Ubuntu 16.04 desktop environment equipped with a GPU

sparklyr 0.7: Spark Pipelines and Machine Learning

R Interface to Google CloudML

We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including on-demand access to training on GPUs and hyperparameter tuning to optimize key attributes of model architectures

tfruns: Tools for TensorFlow Training Runs

The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R

sparklyr 0.6: Distributed R and external sources

sparklyr 0.5: Livy and dplyr improvements

sparklyr — R interface for Apache Spark

RStudio Team

Shiny JavaScript Tutorials

Article Spotlight: Persistent data storage in Shiny apps

New Shiny cheat sheet and video tutorial

SparkR preview by Vincent Warmerdam

Data Visualization cheatsheet, plus Spanish translations

Package Development cheatsheet, plus Chinese translations

Introduction to Data Science with R video workshop

The R Markdown Cheat Sheet

Master interactive documents at the Shiny Dev Center