蘋果 | GOOGLE | 現貨 | 其他 (APPLE | GOOGLE | SPOTIFY | OTHERS)

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

編者按：邁向數據科學播客的“攀登數據科學階梯”系列由杰里米·哈里斯(Jeremie Harris)主持。 杰里米(Jeremie)幫助運營一家名為 SharpestMinds 的數據科學指導創業公司 。 您可以收聽以下播客：

Project-building is the single most important activity that you can get up to if you’re trying to keep your machine learning skills sharp or break into data science. But a project won’t do you much good unless you can show it off effectively and get feedback to iterate on it — and until recently, there weren’t many places you could turn to do that.

如果您要保持機器學習技巧的敏捷性或進入數據科學領域，那么項目構建是您可以從事的最重要的一項活動。 但是，除非您可以有效地炫耀它并獲得反饋以對其進行迭代，否則一個項目不會對您有多大好處-直到最近，您還沒有多少地方可以這樣做。

A recent open-source initiative called MadeWithML is trying to change that, by creating an easily shareable repository of crowdsourced data science and machine learning projects, and its founder, former Apple ML researcher and startup founder Goku Mohandas, sat down with me for this episode of the TDS podcast to discuss data science projects, his experiences doing research in industry, and the MadeWithML project.

最近一個名為MadeWithML的開源計劃正試圖通過創建一個易于共享的眾包數據科學和機器學習項目的存儲庫來改變這一現狀 ，其創始人，前Apple ML研究人員和初創公司創始人Goku Mohandas都與我坐下來TDS播客的一位，討論數據科學項目，他在行業中的研究經驗以及MadeWithML項目。

Here were my favourite take-homes:

這是我最喜歡的帶回家：

• Employers are expecting more and more from machine learning projects. Building a jupyter notebook and using a machine learning model to make interesting predictions just isn’t good enough anymore, and a key step in going beyond this stage is to collect your own data, to ensure that you’re solving a niche problem that other applicants you’re competing with haven’t.
  雇主對機器學習項目的期望越來越高。 建立Jupyter筆記本并使用機器學習模型進行有趣的預測已經遠遠不夠了，超越這一階段的關鍵一步就是收集自己的數據，以確保您正在解決其他人的利基問題。與您競爭的申請人還沒有。
• Another critical step to include in your projects is deployment: it’s really important to wrap up your model in a basic web app that makes it easy to share and show off. The last thing you’ll want to do is introduce yourself to hiring managers by sending them 400 lines of code to review — sending them a deployed web app instead is like giving them a fun toy to play with, and makes it much more likely that they’ll want to engage with you.
  包含在項目中的另一個關鍵步驟是部署：將模型打包到一個基本的Web應用程序中以使其易于共享和展示非常重要。 您要做的最后一件事是向他們介紹招聘經理，方法是向他們發送400行代碼來進行審查-向他們發送已部署的Web應用程序就像給他們一個有趣的玩具，并且更有可能他們想與您互動。
• Machine learning has had an open-source culture from the very beginning, and that’s forced a lot of companies that used to be insular, siloed and even secretive to update their operations in order to be able to draw machine learning talent. Apple in particular has managed that transition well, and Goku related some of the major cultural shifts that were required.
  機器學習從一開始就具有開源文化，這迫使許多以前孤立，孤立甚至秘密的公司來更新其業務，以便吸引機器學習人才。 尤其是蘋果公司，已經很好地完成了這一轉變，悟空(Goku)提出了一些必需的重大文化轉變。
• Many people think that you need a degree in CS to do data science or machine learning, but that couldn’t be further from the truth. As data science has matured, focus has shifted from purely technical skills to business and product skills. It’s no longer enough for data scientists and ML engineers to be able to solve important problems: they now have to be good at identifying problems worth solving. That’s where subject matter expertise can be critical — and that’s something people often start with when they come from non-CS backgrounds. If you’re a former economist, financier, social worker, or you’ve had experience in any particular field, even if it’s not technical, you’re in a great position to understand where ML can be leveraged to solve real problems.
  許多人認為您需要擁有CS學位才能進行數據科學或機器學習，但這離事實還遠。 隨著數據科學的成熟，重點已經從純粹的技術技能轉移到業務和產品技能。 對于數據科學家和ML工程師來說，解決重要問題已不再足夠：他們現在必須善于識別值得解決的問題。 那是主題專業知識至關重要的地方，而這正是人們來自非CS背景時經常要從那里開始的。 如果您是前經濟學家，金融家，社會工作者，或者您有任何特定領域的經驗，即使它不是技術專家，您也很容易理解可以在哪里利用ML解決實際問題。

You can follow Goku on Twitter here, check out Made With ML and their Twitter account, and you can follow me on Twitter here.

您可以在Twitter上關注Goku ，查看Made Made ML 及其Twitter帳戶 ，也可以在Twitter上關注我 。