Julia Silge

Julia Silge - Keynote PyCon Colombia 2025

Julia Silge

Engineering manager at Posit PBC, leading development of open-source software for data science in Python, R. Data scientist, expertise in machine learning, text mining. PhD in astrophysics, author of books on data science.

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Outgrowing your laptop with Positron - Julia Silge

Have you ever run out of memory or time when tidying data, making a visualization, or training a model? An R user may find their laptop more than sufficient to start their journey with statistical computing, but as datasets grow in size and complexity, so does the necessity for more sophisticated tooling. This talk will step through a set of approaches to scale your tasks beyond in-memory analysis on your local machine, using the Postron IDE: adopting a lazy evaluation engine like DuckDB, connecting to remote databases with fluent workflows, and even migrating from desktop analysis entirely to server or cloud compute using SSH tunnelling.

The transition away from a local, in-memory programming paradigm can be challenging for R users, who may not have much exposure to tools or training for these ways of working. This talk will explore available options which make crossing this boundary more approachable, and how they can be used with an advanced development environment suited for statistical computing with R. Integrations in the Positron IDE make all these tasks easier; for example, remote development in Positron allows an R user to seamlessly write code on their local machine, and execute that code on a remote host without tedious interactions outside the IDE. Whether you train statistical models, build interactive apps, or work with large datasets, after this talk you’ll walk away with techniques for doing it better with Positron.

How I got unstuck with Python (Julia Silge, Posit) | posit::conf(2025)

How I got unstuck with Python

Speaker(s): Julia Silge

Abstract:

Python as a language is known for being explicit, simple, readable, and beautiful. At the same time, the tooling around using and writing this language has not always made people feel productive and delighted. I know this has been true for me! In this talk, learn about recent improvements in tooling for Python that have finally addressed my own persistent challenges. Posit’s new IDE, Positron, provides a next generation environment for Python data practice, and this new IDE plays nicely with modern language tooling from the Python community. Whether you are Python curious or looking for ways to improve your Python workflows, hear about how I finally got myself unstuck with the most popular programming language in the world.

Julia Silge - Keynote PyCon Colombia 2025

A first look at Positron - Julia Silge

Description Positron is a next generation data science IDE built by the creators of RStudio. It has been available for beta testing for a number of months, and R users may have wondered if they should try it or if it will be a good fit for them. This new IDE is an extensible tool built to facilitate exploratory data analysis, reproducible authoring, and publishing data artifacts, and it is an IDE that supports but is not built only for R. How should an R user think about Positron, compared to the other options out there?

In this talk, learn about how and why Positron is designed the way it is, what will feel familiar or new coming from other IDEs such as RStudio, and when (or if) people who use R should consider giving it a try. You’ll hear about different choices when it comes to defaults and ways of working, such as how to think about your projects or folders and how to manage multiple versions of R. You will also learn about new functionality for R users and package developers that we have never had before, like new approaches for managing R package tests and the ability to customize an IDE using extensions. If you are curious about Positron and how it fits into the R ecosystem, you’ll come away from this talk with more details about its capabilities and more clarity about whether it may be a good choice for you.

The changing landscape of data science | Kanchana Padmanabhan | Data Science Hangout

Live Q&A following Workflow Demo - January 29th!

This is the Live Q&A session for our Workflow Demo on January 29th on Model cards with vetiver for transparent, responsible reporting with Julia Silge.

Join us for the Demo first with Julia Silge on Jan 29th at 11am ET to learn:

1️⃣ How to get started with your first model card 2️⃣ How a model card fits in with model monitoring 3️⃣ How to use Posit Team to author and publish your model card

Model cards with vetiver for transparent, responsible reporting

Good documentation helps us make sense of software, know when and how to use it, and understand its purpose. The same can be true of documentation or reporting for a deployed model, but it can be hard to know where to start.

The paper “Model Cards for Model Reporting” (Mitchell et al. 2019) provides a suggested framework for organizing and presenting the essential facts about a deployed machine learning model, and the vetiver packages for both R and Python provide templates for getting started with your own model card.

Julia Silge joined us on Jan 29th to share:

1️⃣ How to get started with your first model card 2️⃣ How a model card fits in with model monitoring 3️⃣ How to use Posit Team to author and publish your model card

Introducing Positron, a new data science IDE - posit conf 2024

Positron is a next-generation data science IDE that is newly available to the community for early beta testing. This new IDE is an extensible tool built to facilitate exploratory data analysis, reproducible authoring, and publishing data artifacts. Positron currently supports these data workflows in either or both Python and R and is designed with a forward-looking architecture that can support other data science languages in the future. In this session, learn from the team-building Positron about how and why it is designed the way it is, what will feel familiar or new coming from other IDEs, and whether it might be a good fit for your own work.

Talk by Julia Silge, Isabel Zimmerman, Tom Mock, Jonathan McPherson, Lionel Henry, Davis Vaughan, and Jenny Bryan

So You Think You Can ANALYZE? (Data Content Creator Hackathon)

Watch your favorite data science content creators compete for a piece of history. They will either prove their skills in front of the world or fail in the pursuit of eternal glory. This is so you think you can analyze.

Special thanks to Posit for sponsoring this competition and make this all possible!

Competitors: Team Jack @averysmith Jack Blandin @KeithGalli

Team MMA @AlexTheAnalyst @nerdnourishment @Miki_ML

Team Null Nick Singh Mark Freeman @SeattleDataGuy

Team Shashank @ShashankData

Team Posit-ively Skewed Greg Coquillo @datascienceharp Richad Nieves-Becker Ian Greengross

Positron: An IDE Specialized For Data Science

Dr. Julia Silge, Engineering Manager at Posit, joins @JonKrohnLearns to introduce Positron, a fresh open-source IDE that’s perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful.

817: The Positron IDE, Tidy NLP and MLOps — with Dr. @JuliaSilge

#PositronIDE #Tidyverse #MLOps

Dr. Julia Silge, Engineering Manager at Posit, joins @JonKrohnLearns to introduce the brand-new Positron IDE, perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful.

From Physics PhD to MLOps builder - Julia Silge - The Data Scientist Show #087

Julia Silge⁠ is an engineering manager at Posit PBC, formerly know as R-studio, where she leads a team of developers building open source software MLOps. Before Posit, she finished a PhD in astrophysics, worked for several years in the nonprofit space, and was a data scientist at Stack Overflow where some of her most public work involved the annual developer survey. We talked about MLOps tools, challenges in survey data, text analysis, and balancing her interests in data science and engineering. Subscribe to Daliana’s newsletter on ⁠www.dalianaliu.com⁠ for more on data science and career.

How to develop and deploy a machine learning model with Posit

While data scientists are often taught about training a machine learning model, building a reliable MLOps strategy to deploy and maintain that model can be daunting.

It doesn’t have to be this way! Join us with Julia Silge at Posit on Wednesday, April 24th at 11 am ET to learn how Posit Team provides fluent tooling for the whole ML lifecycle.

1. Develop an ML model using Posit Workbench and a recent Tidy Tuesday dataset!
2. Version, deploy, and monitor that model with Posit Connect
3. Maintain reproducible software dependencies throughout the ML lifecycle with Posit Package Manager

No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo

Reliable Maintenance of Machine Learning Models - posit::conf(2023)

Presented by Julia Silge

Maintaining machine learning models in production can be quite different from maintaining general software projects, because of the unique statistical characteristics of ML models.

In this talk, learn about model drift, the different ways the word “performance” is used with models, what you can monitor about a model, how feedback loops impact models, and how you can use vetiver to set yourself up for success with model maintenance. This talk will help practitioners who are already deploying models, but this is also useful knowledge for practitioners earlier in their MLOps journey; decisions made along the way can make the difference between resilient models that are easier to maintain and disappointing or misleading models.

Julia Silge @ Posit | Data Science Hangout

We were recently joined by Julia Silge, Data Scientist & Software Engineer at Posit, PBC to talk about the skills and tools that data scientists need to deploy their models and scale their impact.

Bio: Julia Silge is a data scientist and software engineer at Posit PBC where she works on open source modeling and MLOps tools. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Emil Hvitfeldt - Slidecraft: The Art of Creating Pretty Presentations

Slidecraft: The Art of Creating Pretty Presentations by Emil Hvitfeldt

posit::conf(2023) Workshop: Deploy and Maintain Models with vetiver

Data Science in People Analytics | Led by Elizabeth Esarove, AT&T

People are the face, heart, and hands of a company. In people analytics, we analyze data to reveal actionable insights that provide evidence for decisions regarding employees, work, and business objectives. This talk will cover the use of data science for people analytics projects such as workforce planning, improving employee engagement, and retaining talent.

Speaker bio: Elizabeth Esarove is a data scientist in People Analytics at AT&T. In her role, Elizabeth is part of a larger team focused on embedding data and analytics into the root of decision-making and transforming insights into actionable solutions that improve employee outcomes and drive business value.

Timestamps: *Q&A timestamps listed further below 3:42 - Start of session 5:14 - What is People Analytics 6:26 - Opportunities for Data Science in People Analytics 7:10 - Using Predictive Models to Reduce Attrition 11:10 - Segmenting Your Population 18:55 - Communicating with Leaders 20:11 - Time Series Forecasting for Workforce Changes 24:41 - Analyzing Employee Survey Comments

Helpful Resources Below: *more follow-up to come with a Q&A blog post in the works

Julia Silge & Max Kuhn | Good Practices for Applied Machine Learning | Posit (2022)

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. Whether you are just starting out today or have years of experience with ML, tidymodels offers a consistent, flexible framework for your work. In this talk, learn how tidymodels has been designed to promote ergonomic, effective, and safe modeling practice. We will discuss how to think about the steps of building a model from beginning to end, how to fluently use different modeling and feature engineering approaches, how to avoid common pitfalls of modeling like overfitting and data leakage, and how to version and deploy reliable models trained in R.

MLOps with vetiver in Python and R | Led by Julia Silge & Isabel Zimmerman

Many data scientists understand what goes into training a machine learning model, but creating a strategy to deploy and maintain that model can be daunting. In this meetup, learn what MLOps is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. See how to get started with vetiver, a framework for MLOps tasks in R and Python that provides fluent tooling to version, deploy, and monitor your models.

Creating Features for Machine Learning from Text – Julia Silge, March 2022

Julia Silge is a software engineer at RStudio PBC where she works on open source modeling tools. She holds a PhD in astrophysics and has worked as a data scientist in tech and the nonprofit sector, as well as a technical advisory committee member for the US Bureau of Labor Statistics. She is an author, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Natural language that we as speakers and writers use must be dramatically transformed to new representations for analysis, whether we are just starting off with exploratory data analysis or are ready to train machine learning algorithms such as predictive models. We can explore typical text preprocessing steps from the ground up, from tokenization to building word embeddings, and consider the effects of these steps. When are these preprocessing steps helpful, and when are they not? In this talk, learn about the process of text preprocessing for ML models in the real world, how and when practitioners use different preprocessing choices, and considerations for text ML tooling.

Dr. Julia Silge | RStudio Voices | RStudio

Julia Silge recently sat down with Michael Demsko Jr for an interview, the first in a new Voices of RStudio PBC series.

In this excerpt, Julia discusses where she sees the most value created in the data science lifecycle–and it’s not advanced machine learning models.

Julia Silge | Monitoring Model Performance | RStudio

0:00 Project introduction 1:50 Overview of the setup code chunk 3:05 Getting new data 4:05 Getting model from RStudio Connect using httr and jsonlite 6:20 Bringing in metrics 9:45 Using the pins package 10:50 Using boards on RStudio Connect 13:30 Benefits of using pins 14:00 Visualizations using ggplot and plotly 17:00 Knitting the flexdashboard 18:10 Project takeaways

David Robinson | The unreasonable effectiveness of public work | RStudio (2019)

In this talk, I’ll lay out the reasons that blogging, open source contribution, and other forms of public work are a critical part of a data science career. For beginners, a blog is a great accompaniment to data science coursework and tutorials, since it gives you experience applying practical data science skills to real problems. For data scientists at any stage of their careers, open source development offers practice in collaboration, documentation, and interface design that complement other kinds of software development. And for data scientists more advanced in their careers, writing a book is a great way to crystallize your expertise and ensure others can build on it. All of these practices build skills in communication and collaboration that form an essential component of data science work. Each also lets you build a public portfolio of your skills, get feedback from your peers, and network with the larger data science community.