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    <title>Tania Allard</title>
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      <title>Keeping Research Software Relevant  for Tomorrow </title>
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      <title>2024_pydata_lndn.pdf</title>
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      <title>The RSE hiring and career progression pipelines: Top tips to navigate them efficiently</title>
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      <title>Mentored Sprints - 2023</title>
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      <title>Mentored Sprints 2022 - kickoff</title>
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      <pubDate>Sat, 30 Apr 2022 00:00:00 -0400</pubDate>
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      <title>Como participar en el mercado emergente del codigo abierto</title>
      <description></description>
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      <pubDate>Tue, 22 Mar 2022 00:00:00 -0400</pubDate>
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      <title>El presente y futuro del computo cientifico con Python</title>
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      <pubDate>Sat, 11 Dec 2021 00:00:00 -0500</pubDate>
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      <title>Foss for fun and profit</title>
      <description>Keynote talk for PyCon Chile</description>
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      <content:encoded>Keynote talk for PyCon Chile</content:encoded>
      <pubDate>Sat, 06 Nov 2021 00:00:00 -0400</pubDate>
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      <title>Open source for fun and profit: rethinking the long road of sustainability.</title>
      <description>Open source for fun and profit: rethinking the long road of sustainability.

It is not an understatement to say that today, in 2021, the world runs on open source. Wherever you look, you will find open-source software in the wild - whether you have a smartphone in your pocket or if you see a self-driving car passing by or rockets orbiting in space, open-source is even in simple tasks as deciding what to watch tonight. Open-source software and open infrastructure not only serve as the engine that powers our world. It also enables folks to get together and connect isolated individuals, communities, and missions.

With this in mind, the most important questions we should be asking is: who and what fuels open source? In this presentation, I will discuss how open source can bring individuals together to form communities. And more importantly, how we can rethink open-source and labour to ensure its long-term sustainability.</description>
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      <content:encoded>Open source for fun and profit: rethinking the long road of sustainability.

It is not an understatement to say that today, in 2021, the world runs on open source. Wherever you look, you will find open-source software in the wild - whether you have a smartphone in your pocket or if you see a self-driving car passing by or rockets orbiting in space, open-source is even in simple tasks as deciding what to watch tonight. Open-source software and open infrastructure not only serve as the engine that powers our world. It also enables folks to get together and connect isolated individuals, communities, and missions.

With this in mind, the most important questions we should be asking is: who and what fuels open source? In this presentation, I will discuss how open source can bring individuals together to form communities. And more importantly, how we can rethink open-source and labour to ensure its long-term sustainability.</content:encoded>
      <pubDate>Tue, 19 Oct 2021 00:00:00 -0400</pubDate>
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      <title>Docker and Python: making them play nicely and securely for Ml and DS</title>
      <description>Docker has become a standard tool for developers around the world to deploy applications in a reproducible and robust manner. The existence of Docker and Docker compose have reduced the time needed to set up new software and implementing complex technology stacks for our applications.

Now, six years after the initial release of` Docker, we can say with confidence that containers and containers orchestration have become some of the defaults in the current technology stacks.

There are thousands of tutorials and getting started documents for those wanting to adopt Docker for apps deployment. However, if you are a Data Scientist, a researcher or someone working on scientific computing wanting to adopt Docker, the story is quite different. There are very few tutorials (in comparison to app/web) and documents focused on Docker best practices for DS and scientific computing. If you are working on DS, ML or scientific computing, this talk is for you. We’ll cover best practices when building Docker containers for data-intensive applications, from optimising your image build, to ensuring your containers are secure and efficient deployment workflows. We will talk about the most common problems faced while using Docker with data-intensive applications and how you can overcome most of them. Finally, I’ll give some practical and useful tips for you to improve your Docker workflows and practises.

Attendees will leave the talk feeling confident about adopting Docker across a range of DS, ML and research projects.</description>
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      <content:encoded>Docker has become a standard tool for developers around the world to deploy applications in a reproducible and robust manner. The existence of Docker and Docker compose have reduced the time needed to set up new software and implementing complex technology stacks for our applications.

Now, six years after the initial release of` Docker, we can say with confidence that containers and containers orchestration have become some of the defaults in the current technology stacks.

There are thousands of tutorials and getting started documents for those wanting to adopt Docker for apps deployment. However, if you are a Data Scientist, a researcher or someone working on scientific computing wanting to adopt Docker, the story is quite different. There are very few tutorials (in comparison to app/web) and documents focused on Docker best practices for DS and scientific computing. If you are working on DS, ML or scientific computing, this talk is for you. We’ll cover best practices when building Docker containers for data-intensive applications, from optimising your image build, to ensuring your containers are secure and efficient deployment workflows. We will talk about the most common problems faced while using Docker with data-intensive applications and how you can overcome most of them. Finally, I’ll give some practical and useful tips for you to improve your Docker workflows and practises.

Attendees will leave the talk feeling confident about adopting Docker across a range of DS, ML and research projects.</content:encoded>
      <pubDate>Tue, 20 Oct 2020 00:00:00 -0400</pubDate>
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      <title>Python and Docker for ML and Data Science</title>
      <description></description>
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      <pubDate>Sat, 26 Sep 2020 00:00:00 -0400</pubDate>
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      <title>DevOps for data science: automate the boring stuff and leverage the OSS ecosystem</title>
      <description></description>
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      <content:encoded></content:encoded>
      <pubDate>Thu, 06 Aug 2020 00:00:00 -0400</pubDate>
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      <title>Docker and Python </title>
      <description>Making them play nicely and securely for Data Science and Machine learning</description>
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      <content:encoded>Making them play nicely and securely for Data Science and Machine learning</content:encoded>
      <pubDate>Thu, 23 Jul 2020 00:00:00 -0400</pubDate>
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      <title>Handbook for successful online Open source sprints</title>
      <description>We have organized many mentored sprints, but being in lockdown meant we had to run our first online mentored sprints. Moving such events from in-person to an online setting was a challenging yet quite enlightening experience. To start with, there are many things that work in traditional sprints but does not work in an online format. Learnt from our experience, so that you do not have to start from scratch. In this talk, we are going to tell you what challenges you will face organizing an online event, and how to overcome them, from making the right technical choices to supporting sprinters and open-source mentors.</description>
      <media:content url="https://files.speakerdeck.com/presentations/ceeaad6bd2e04c5eab55995b3e6d90e0/preview_slide_0.jpg?15809585" type="image/jpeg" medium="image"/>
      <content:encoded>We have organized many mentored sprints, but being in lockdown meant we had to run our first online mentored sprints. Moving such events from in-person to an online setting was a challenging yet quite enlightening experience. To start with, there are many things that work in traditional sprints but does not work in an online format. Learnt from our experience, so that you do not have to start from scratch. In this talk, we are going to tell you what challenges you will face organizing an online event, and how to overcome them, from making the right technical choices to supporting sprinters and open-source mentors.</content:encoded>
      <pubDate>Thu, 09 Jul 2020 00:00:00 -0400</pubDate>
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      <title>Docker and Python: making them play nicely and securely for Data Science and Machine Learning</title>
      <description>"Docker containers are a popular way to create reproducible development environments without having to install complex dependencies on your local machine.  Developers all over the world use them for production and R&amp;D environments. 

However,  using Docker for Machine Learning is not always straightforward. Plus most of the tutorials and content out there focus on how to use Docker to containerize apps rather than focusing on Data Science solutions.

In this talk, Tania shares some tips and tricks on how to effectively use Docker for Machine Learning and Data Science, helping to make your work more robust and reproducible."</description>
      <media:content url="https://files.speakerdeck.com/presentations/ec6636850f374cf293becc60b24f7849/preview_slide_0.jpg?15409268" type="image/jpeg" medium="image"/>
      <content:encoded>"Docker containers are a popular way to create reproducible development environments without having to install complex dependencies on your local machine.  Developers all over the world use them for production and R&amp;D environments. 

However,  using Docker for Machine Learning is not always straightforward. Plus most of the tutorials and content out there focus on how to use Docker to containerize apps rather than focusing on Data Science solutions.

In this talk, Tania shares some tips and tricks on how to effectively use Docker for Machine Learning and Data Science, helping to make your work more robust and reproducible."</content:encoded>
      <pubDate>Mon, 04 May 2020 00:00:00 -0400</pubDate>
      <link>https://speakerdeck.com/trallard/docker-and-python-making-them-play-nicely-and-securely-for-data-science-and-machine-learning</link>
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      <title>Easy Data Processing on Azure with Serverless Functions</title>
      <description>Serverless or Function as a Service (FaaS) enables you to focus more on your code while making it easier to deploy your applications. FaaS can be great tools to perform data processing scenarios. Azure functions allow you to leverage Azure's robust, managed, and scalable cloud computing platform. In this workshop, Tania Allard will teach you how you can get started with Azure functions and Python for data processing scenarios.

✨ Check the tutorial at https://aka.ms/pycon2020-azurefunctions

✨ GitHub repository https://github.com/trallard/pycon2020-azure-functions

🐍  PyCon schedule description: https://us.pycon.org/2020/schedule/presentation/230/
</description>
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      <content:encoded>Serverless or Function as a Service (FaaS) enables you to focus more on your code while making it easier to deploy your applications. FaaS can be great tools to perform data processing scenarios. Azure functions allow you to leverage Azure's robust, managed, and scalable cloud computing platform. In this workshop, Tania Allard will teach you how you can get started with Azure functions and Python for data processing scenarios.

✨ Check the tutorial at https://aka.ms/pycon2020-azurefunctions

✨ GitHub repository https://github.com/trallard/pycon2020-azure-functions

🐍  PyCon schedule description: https://us.pycon.org/2020/schedule/presentation/230/
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      <pubDate>Fri, 24 Apr 2020 00:00:00 -0400</pubDate>
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      <title>Mentored sprints for diverse beginners: a retrospective and future plans</title>
      <description>Open source relies heavily on the community for its sustainability and success. As a result, multiple initiatives aim to bring together community members, developers and open source projects in the form of conference sprints, yearly events (e.g. Hacktoberfest) or unconferences.  However, many projects currently struggle to engage with a diverse set of contributors in the long term.
Over the last year and a half, I have been working on running mentored sprints for diverse beginners as conference events and as standalone sprints over the year. 
This unique approach has proven very successful, so much that these sprints are becoming a permanent event at PyCon US and PyCon UK and are being adopted by other conferences across the world. 
In this talk, I will share my learnings from running this kind of sprints, the logistics behind them, some impact stories,  and the plans for the future of the mentored sprints and how you and your community can get involved.</description>
      <media:content url="https://files.speakerdeck.com/presentations/0313ad40c6814416aeba537ab44df57b/preview_slide_0.jpg?14438213" type="image/jpeg" medium="image"/>
      <content:encoded>Open source relies heavily on the community for its sustainability and success. As a result, multiple initiatives aim to bring together community members, developers and open source projects in the form of conference sprints, yearly events (e.g. Hacktoberfest) or unconferences.  However, many projects currently struggle to engage with a diverse set of contributors in the long term.
Over the last year and a half, I have been working on running mentored sprints for diverse beginners as conference events and as standalone sprints over the year. 
This unique approach has proven very successful, so much that these sprints are becoming a permanent event at PyCon US and PyCon UK and are being adopted by other conferences across the world. 
In this talk, I will share my learnings from running this kind of sprints, the logistics behind them, some impact stories,  and the plans for the future of the mentored sprints and how you and your community can get involved.</content:encoded>
      <pubDate>Wed, 11 Dec 2019 00:00:00 -0500</pubDate>
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      <title>How can Azure superpower your data science journey?</title>
      <description>This deck serves as an introduction to Azure Machine learning services. 
it walks you through a brief intro of ML, Azure Machine learning services, Distributed hyperparameter tuning and Auto ML</description>
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      <content:encoded>This deck serves as an introduction to Azure Machine learning services. 
it walks you through a brief intro of ML, Azure Machine learning services, Distributed hyperparameter tuning and Auto ML</content:encoded>
      <pubDate>Sat, 30 Nov 2019 00:00:00 -0500</pubDate>
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