Sample Space

probabl
  • 5.0(3개의 평가)
  • 과학 기술
  • 매월 업데이트
Sample Space

Sample space is a podcast about tools, thoughts and techniques from machine learning practitioners. We talk to toolmakers and practitioners about interesting problems in the real world to find out how great ideas in our field actually manifest.

에피소드

  1. You want to be in control of your own Copilot

    11월 6일

    You want to be in control of your own Copilot

    There are many LLMs that you can use for programming these days. Some of them even go into your IDE like Cursor or Github Copilot. But what if you want to tweak these LLMs do to what you want? Instead of being stuck with the tools that a vendor gives you, the goal of Continue.dev is to allow you to customise this yourself. In this podcast we talk to Ty Dunn, co-founder of the project to learn more about this. If you are curious to learn more about this effort, please check out https://continue.dev. You may always want to read the manifesto over at https://amplified.dev/. We have a Discord these days, feel free to discuss the podcast with us there! https://discord.probabl.ai This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ Bluesky: https://bsky.app/profile/probabl.bsky.social LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai

    1시간 7분
  2. What it is like to maintain the scikit-learn docs

    10월 31일

    What it is like to maintain the scikit-learn docs

    Scikit-learn's documentation pages are celebrated. But not everyone is aware that the project actually has somebody on payroll to take care of it. In this episode we talk to Arturo about stories from the scikit-learn documentation. In particular, the docs have a recommender that few folks are aware of. People just assume that it is manually curated, but there are a few base scikit-learn tools under the hood there. Link to the official scikit-learn MOOC: https://inria.github.io/scikit-learn-mooc/ We have a Discord these days, feel free to discuss the podcast with us there! https://discord.probabl.ai You can follow the podcast on most podcast players including apple podcasts, spotify and rss.com. - https://podcasts.apple.com/us/podcast/sample-space/id1739598572 - https://open.spotify.com/show/0BnwEHuyOlHgeZfselpn1n - https://rss.com/podcasts/sample-space/ This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ Bluesky: https://bsky.app/profile/probabl.bsky.social LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai

    55분
  3. Sqlite can totally do embeddings now

    10월 23일

    Sqlite can totally do embeddings now

    Vector databases are kind of everywhere these days. There is a big pool of VC's that are pooring money into the ecosystem too. But while all of that is happening, sqlite has also gotten support for it. In this episode we talk the Alex Garcia, the maintainer of this project, and discuss how the project got created on what the future has in store. Sqlite-vec Github repo: https://github.com/asg017/sqlite-vec Alex Garcia blog: https://alexgarcia.xyz/blog/2024/sqlite-vec-hybrid-search/index.html Datasette discord: https://discord.com/invite/ktd74dm5mw Sqlite-vec channel on Mozilla Discord: https://discord.gg/Ve7WeCJFXk

    59분
  4. How to rethink the notebook - with Akshay Agrawal, co-creator of Marimo

    10월 16일

    How to rethink the notebook - with Akshay Agrawal, co-creator of Marimo

    Jupyter has been a great environment to explore computational ideas, but that doesn't mean that it can be the only environment for interactive coding in Python. It also comes with some downsides, which led Akshay Agrawal to create an alternative called Marimo. We discussed it in a previous livestream and figured that it was time to sit down with the creator to learn what led to the development of this exciting new too. You can learn more about Marimo by going to their website over at https://marimo.io To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai

    1시간 12분
  5. You are always dealing with many tables - with Madelon Hulsebos

    9월 10일

    You are always dealing with many tables - with Madelon Hulsebos

    When you are working on a data pipeline for ML ... you are never dealing with a single table. It always demands different tables for different reasons that all have to be mashed together in order to have something that you can learn from. But if that is the case, why do we spend so much time talking about ML pipelines that only work on a single table? Madelon Hulsebos has a Phd on the topic and so we figured that we might ask her. As mentioned in the podcast, here is the link to Madelon's homepage. https://www.madelonhulsebos.com/ Some links to interesting articles from Madelon, as well as her homepage, can be found below. https://www.madelonhulsebos.com/assets/dataset_search_survey.pdf https://dl.acm.org/doi/pdf/10.1145/3654975 https://dl.acm.org/doi/pdf/10.1145/3588710

    1시간 9분
  6. How Narwhals has many end users ... that never use it directly with Marco Gorelli

    8월 21일

    How Narwhals has many end users ... that never use it directly with Marco Gorelli

    When you pip install a package you will for sure end up using it later. But often you will also install a bunch of dependencies and it is very likely that you won't directly interact with all of them. That does not mean that such a package is not useful, it merely means that the package might be directly used by a maintainer instead. This is interesting, because recently one such tool came into existence. It is called Narwhals and it seems to be on track to become critical infrastructure for data science projects. We have the maintainer of Narwhals on the show this week to talk about it. To learn more about Narwhals, you can check the repository here: https://github.com/narwhals-dev/narwhals This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai

    1시간 1분
  7. Pragmatic data science checklists with Peter Bull - cofounder Drivendata

    7월 17일

    Pragmatic data science checklists with Peter Bull - cofounder Drivendata

    A lot of things can (and have) gone wrong when folks tried to apply data science projects. So how might we prevent that? Maybe what we need to do is to look at the medical profession and their practice of checklists before surgery.

    1시간 6분
  8. Model safety, that's a pickle! with Adrin Jalali - scikit-learn maintainer

    6월 27일

    Model safety, that's a pickle! with Adrin Jalali - scikit-learn maintainer

    Historically it's always been the case that you would use a pickle file to store a trained scikit-learn model on disk for deployment. Pickles make sense because these are so flexible, but they do carry a security concern. Adrin has been working on a remedy called skops, which is the main topic of this podcast. To learn more about skops, make sure to check the documentation: https://skops.readthedocs.io/en/stable/

    1시간 2분
  9. Moving Towards KDearestNeighbors with Leland McInnes - creator of UMAP

    5월 30일

    Moving Towards KDearestNeighbors with Leland McInnes - creator of UMAP

    Leland McInnes is known for a lot of packages. There's UMAP, but also PyNNDescent and HDBScan. Recently he's also been working on tools to help visualise clusters of data and he's also cooking up something new that's related to nearest neighbor algorithms. This interview touches all of these topics. If you're interested in learning more about the MoMA exhibition, it was by Refik Anadol: https://refikanadol.com/ and this was the work at MoMA: https://refikanadol.com/works/unsupervised/. The other artist was Kyle McDonald: https://kylemcdonald.net/ and the piece we mentioned was this one: https://www.youtube.com/watch?v=04DqdT0-NtI.

    57분
  10. Talk like a DataFrame, run like SQL with Phillip Cloud - core-committer on Ibis

    5월 2일

    Talk like a DataFrame, run like SQL with Phillip Cloud - core-committer on Ibis

    Ibis is a Python library that offers a single data-frame API, from Python, which can run your queries on many different backends. These include databases like Postgres, but also commercial vendors like BigQuery and Snowflake. This ability to control multiple backends from a single API has a lot of use-cases, as well as maintainer challenges, all of which are discussed in this episode. To learn more about Ibis, check out the docs here: https://ibis-project.org/ If you're attending PyCon US this year, you may be interested in Philip's talk: https://us.pycon.org/2024/schedule/presentation/55/ During the podcast, Philip also mentioned a blogpost about DuckDB, here: https://ibis-project.org/posts/why-duckdb/ There was also a dogfooding blogpost, which is this one: https://ibis-project.org/posts/ci-analysis/

    1시간 4분
  11. Enhancing Jupyter with Widgets with Trevor Manz - creator of anywidget.

    4월 11일

    Enhancing Jupyter with Widgets with Trevor Manz - creator of anywidget.

    In this (first!) episode of Sample Space we talk to Trevor Mantz, the creator of anywidget. It's a (neat!) tool to help you build more interactive notebooks by giving you tools to apply just enough Javascript to get directional communication working in your favorite notebook environment. That means that Python can talk to widgets, but also that widgets can talk to Python. There's a lot to like about these widgets and we're doing a proper deep dive in this first episode. To learn more about anywidget, check out the docs. In particular you may want to glance at the gallery first, it has loads of nice examples. You can also find the project on Github and if you're eager to talk to folks involved with the project, consider joining the discord here.

    1시간 12분

  12. 시즌1 예고편

    Introducing Sample Space

    We're starting a new podcast!

    2분

예고편

평가 및 리뷰

5
최고 5점
3개의 평가

소개

Sample space is a podcast about tools, thoughts and techniques from machine learning practitioners. We talk to toolmakers and practitioners about interesting problems in the real world to find out how great ideas in our field actually manifest.

정보

  • 제작진
    probabl
  • 방송 연도
    2024년
  • 에피소드
    12
  • 등급
    전체 연령 사용가
  • 저작권
    © probabl
  • 웹사이트 보기
    Sample Space

좋아할 만한 다른 항목

무삭제판 에피소드를 청취하려면 로그인하십시오.

이 프로그램의 최신 정보 받기

프로그램을 팔로우하고, 에피소드를 저장하고, 최신 소식을 받아보려면 로그인하거나 가입하십시오.
국가 또는 지역 선택

아프리카, 중동 및 인도

아시아 태평양

유럽

라틴 아메리카 및 카리브해

미국 및 캐나다