Machine learning operations (MLOps) are a set of best practices for running machine learning models successfully in production environments. Data scientists and system administrators have expanding options for setting up their pipeline. However, while many tools exist for preparing data and training models, there is a lack of streamlined tooling for tasks like putting a model in production, maintaining the model, or monitoring performance. Enter vetiver, an open-source framework for the entire model lifecycle. Vetiver provides R and Python programmers with a fluid, unified way of working with machine learning models.

Machine learning operations (MLOps) are a set of best practices for running machine learning models successfully in production environments. Data scientists and system administrators have expanding options for setting up their pipeline. However, while many tools exist for preparing data and training models, there is a lack of streamlined tooling for tasks like putting a model in production, maintaining the model, or monitoring performance. Enter vetiver, an open-source framework for the entire model lifecycle. Vetiver provides R and Python programmers with a fluid, unified way of working with machine learning models.

I'm delighted to announce that 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. You can pin objects to a variety of pin boards, including folders (to share on a networked drive or with services like DropBox), RStudio Connect, Amazon S3, and Azure blob storage. Pins can be versioned, making it straightforward to track changes, re-run analyses on historical data, and undo mistakes. Our users have found numerous ways to use this ability to fluently share and version data and other objects, such as automating ETL for a Shiny app.

A new version of pins is available on CRAN today, which adds support for versioning your datasets and DigitalOcean Spaces boards! As a quick recap, the pins package allows you to cache, discover and share resources. You can use pins in a wide range of situations, from downloading a dataset from a URL to creating complex automation workflows (learn more at pins.rstudio.com). You can also use pins in combination with TensorFlow and Keras; for instance, use cloudml to train models in cloud GPUs, but rather than manually copying files into the GPU instance, you can store them as pins directly from R. You can find a detailed list of improvements in the pins NEWS file. To illustrate the new versioning functionality, let's start by downloading and caching a remote dataset with pins.

As more organizations migrate their data science work to the cloud, they naturally want to bring along their favorite data science tools, including RStudio, R, and Python. While RStudio provides many different ways to support an organization's cloud strategyOpens a new window, we've heard from many customers who also use Amazon SageMaker. They wanted an easier way to combine RStudio's professional products with SageMaker's rich machine learning and deep learning capabilities, and to incorporate RStudio into their data science infrastructure on SageMaker. Based on this feedback, we are excited to announce RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. Amazon SageMakerOpens a new window helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.