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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(shù)據(jù)科學(xué)播客的“攀登數(shù)據(jù)科學(xué)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(shù)據(jù)科學(xué)指導(dǎo)創(chuàng)業(yè)公司 。 您可以收聽以下播客：

演示地址

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(xué)習(xí)技巧的敏捷性或進(jìn)入數(shù)據(jù)科學(xué)領(lǐng)域，那么項目構(gòu)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(jìn)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(chuàng)建一個易于共享的眾包數(shù)據(jù)科學(xué)和機器學(xué)習(xí)項目的存儲庫來改變這一現(xiàn)狀 ，其創(chuàng)始人，前Apple ML研究人員和初創(chuàng)公司創(chuàng)始人Goku Mohandas都與我坐下來TDS播客的一位，討論數(shù)據(jù)科學(xué)項目，他在行業(yè)中的研究經(jīng)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(xué)習(xí)項目的期望越來越高。 建立Jupyter筆記本并使用機器學(xué)習(xí)模型進(jìn)行有趣的預(yù)測已經(jīng)遠(yuǎn)遠(yuǎn)不夠了，超越這一階段的關(guān)鍵一步就是收集自己的數(shù)據(jù)，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(guān)鍵步驟是部署：將模型打包到一個基本的Web應(yīng)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(jīng)理，方法是向他們發(fā)送400行代碼來進(jìn)行審查-向他們發(fā)送已部署的Web應(yīng)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(xué)習(xí)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(yè)務(wù)，以便吸引機器學(xué)習(xí)人才。 尤其是蘋果公司，已經(jīng)很好地完成了這一轉(zhuǎn)變，悟空(Goku)提出了一些必需的重大文化轉(zhuǎn)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(rèn)為您需要擁有CS學(xué)位才能進(jìn)行數(shù)據(jù)科學(xué)或機器學(xué)習(xí)，但這離事實還遠(yuǎn)。 隨著數(shù)據(jù)科學(xué)的成熟，重點已經(jīng)從純粹的技術(shù)技能轉(zhuǎn)移到業(yè)務(wù)和產(chǎn)品技能。 對于數(shù)據(jù)科學(xué)家和ML工程師來說，解決重要問題已不再足夠：他們現(xiàn)在必須善于識別值得解決的問題。 那是主題專業(yè)知識至關(guān)重要的地方，而這正是人們來自非CS背景時經(jīng)常要從那里開始的。 如果您是前經(jīng)濟學(xué)家，金融家，社會工作者，或者您有任何特定領(lǐng)域的經(jīng)驗，即使它不是技術(shù)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(guān)注Goku ，查看Made Made ML 及其Twitter帳戶 ，也可以在Twitter上關(guān)注我 。

翻譯自: https://towardsdatascience.com/industry-research-and-how-to-show-off-your-projects-6aa2bfebf01a

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