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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:

编者按：迈向数据科学播客的“攀登数据科学阶梯”系列由杰里米·哈里斯(Jeremie Harris)主持。 杰里米(Jeremie)帮助运营一家名为 SharpestMinds 的数据科学指导创业公司 。 您可以收听以下播客：

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.

如果您要保持机器学习技巧的敏捷性或进入数据科学领域，那么项目构建是您可以从事的最重要的一项活动。 但是，除非您可以有效地炫耀它并获得反馈以对其进行迭代，否则一个项目不会对您有多大好处-直到最近，您还没有多少地方可以这样做。

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.

最近一个名为MadeWithML的开源计划正试图通过创建一个易于共享的众包数据科学和机器学习项目的存储库来改变这一现状 ，其创始人，前Apple ML研究人员和初创公司创始人Goku Mohandas都与我坐下来TDS播客的一位，讨论数据科学项目，他在行业中的研究经验以及MadeWithML项目。

Here were my favourite take-homes:

这是我最喜欢的带回家：

• 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.雇主对机器学习项目的期望越来越高。 建立Jupyter笔记本并使用机器学习模型进行有趣的预测已经远远不够了，超越这一阶段的关键一步就是收集自己的数据，以确保您正在解决其他人的利基问题。与您竞争的申请人还没有。
• 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.包含在项目中的另一个关键步骤是部署：将模型打包到一个基本的Web应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们介绍招聘经理，方法是向他们发送400行代码来进行审查-向他们发送已部署的Web应用程序就像给他们一个有趣的玩具，并且更有可能他们想与您互动。
• 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.机器学习从一开始就具有开源文化，这迫使许多以前孤立，孤立甚至秘密的公司来更新其业务，以便吸引机器学习人才。 尤其是苹果公司，已经很好地完成了这一转变，悟空(Goku)提出了一些必需的重大文化转变。
• 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.许多人认为您需要拥有CS学位才能进行数据科学或机器学习，但这离事实还远。 随着数据科学的成熟，重点已经从纯粹的技术技能转移到业务和产品技能。 对于数据科学家和ML工程师来说，解决重要问题已不再足够：他们现在必须善于识别值得解决的问题。 那是主题专业知识至关重要的地方，而这正是人们来自非CS背景时经常要从那里开始的。 如果您是前经济学家，金融家，社会工作者，或者您有任何特定领域的经验，即使它不是技术专家，您也很容易理解可以在哪里利用ML解决实际问题。

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上关注Goku ，查看Made Made ML 及其Twitter帐户 ，也可以在Twitter上关注我 。