什么和为什么 (The What and Why)

Amazon has long been striving to fix the issue of excess demand (vs supply) of individuals who have proficiency across the fields both Machine Learning and Software Engineering. To date, they have developed sloths of internal resources to get employees up to speed on the essentials. This is typically referred to as OJT, for “on the job training.”

长期以来，亚马逊一直在努力解决在机器学习和软件工程领域精通个人的过剩需求(相对于供应)的问题。 迄今为止，他们已经开发了内部资源懒惰工具，以使员工快速掌握基本要素。 通常将其称为OJT，用于“在职培训”。

OJT only goes so far — the size of your workforce. Aside from hired workers, companies depend on the education system to routinely supply capable talent to the workforce. This system has performed sufficiently for hundreds of years. However, the tide is turning. The speed of machine learning’s integration into industry workflows has largely outpaced the education system’s ability to provide fully-equipped talent. This is partially due to large systems necessarily moving slowly, but also due to a lack of convergence of dominant algorithms and tools in the field. Education systems are basically faced with a choice between overfitting on current trends versus sticking with classical techniques and allowing for OJT to solve the last-mile problem.

OJT只能走这么远–员工人数庞大。 除了聘用的工人外，公司还依靠教育系统来定期向劳动力提供有能力的人才。 该系统已经运行了数百年。 但是，潮流正在转变。 机器学习集成到行业工作流中的速度大大超过了教育系统提供设备齐全的人才的能力。 这部分是由于大型系统必须缓慢移动，也归因于该领域主要算法和工具缺乏收敛性。 从根本上讲，教育系统面临的选择是：过时地适应当前趋势；坚持传统技术；以及让OJT解决最后一英里的问题。

亚马逊的举动 (Amazon’s Take)

Amazon has a great idea — meet halfway.

亚马逊有一个好主意-半途而废。

Academic institutions will largely lean towards proven classical techniques for education, and that is the correct move. To help the last-mile OJT problem even more than post-hire education, Amazon is now making available course materials from their internal “ML University”. By doing this, they will be able to educate many eventual employees even before it is interview time. This helps both sides of the table. Prospective employees can learn much more relevant material ahead of job applications and feel more equipped in job selection and commitment. On the flip side, Amazon and similar companies can then judge talent more directly in interviews than they have been able to. Since so much learning material is publicly available, there is less room for “the benefit of the doubt” when an applicant does not have experience in a certain sub-area.

学术机构将在很大程度上倾向于成熟的古典教育技术，这是正确的做法。 为了进一步解决最后一刻的OJT问题，而不是雇用后教育，亚马逊现在从其内部的“ ML University”提供课程材料。 这样，他们甚至可以在面试时间之前就培训许多最终员工。 这有助于桌子的两面。 准员工可以在申请工作之前学习更多相关材料，并感到他们在选择工作和承诺方面更有能力。 另一方面，亚马逊和类似公司可以比以往更直接地在面试中判断人才。 由于公开提供了如此多的学习材料，因此当申请人在某个分区没有经验时，“怀疑的好处”的空间就更少了。

Just three courses have been released for immediate use: natural language, computer vision, and tabular data. However, more will be rolling out through the end of 2020, with the start of 2021 having all the material public.

仅发布了三门课程供立即使用：自然语言，计算机视觉和表格数据。 但是，随着所有材料的公开发布，到2021年初，将会有更多的内容在2020年底推出。

“By going public with the classes, we are contributing to the scientific community on the topic of machine learning, and making machine learning more democratic,” Werness adds. “This field isn’t limited to individuals with advanced science degrees, or technical backgrounds. This initiative to bring our courseware online represents a step toward lowering barriers for software developers, students and other builders who want to get started with practical machine learning.”- Amazon Science

“通过在课堂上公开亮相，我们为机器学习这一主题的科学界做出了贡献，并使机器学习更加民主，” Werness补充说。 “该领域不仅限于具有高级科学学位或技术背景的个人。 这项使我们的课件上线的举措代表了降低障碍的一步，这对希望开始实际机器学习的软件开发人员，学生和其他构建者来说是一个障碍。”- Amazon Science

Check out the intro to the “Accelerated Computer Vision” course below. The entire course is available on similar Youtube pages.

查看下面的“加速计算机视觉”课程的介绍。 整个课程可在类似的YouTube页面上找到。

意见和注意事项 (Opinions and Cautions)

This is great for the democratization of machine learning in the industry. Academic has long been very open and cooperative with ML research. The same can be said for the open-source software movement. Recently, in the past decade or so, we have seen these ideologies extend into the ML industry space. Its continuation will ensure that the economy’s aggregate output will rise, while still fostering healthy competition.

这对于行业中机器学习的民主化非常重要。 长期以来，学术界非常开放，并与ML研究合作。 开源软件运动也可以这样说。 最近，在过去的十年左右的时间里，我们已经看到这些思想扩展到ML工业领域。 持续进行下去将确保经济总量增长，同时仍可促进健康的竞争。

I’ll add a word of caution, however. The phenomenon referred to as “vendor lock-in” occurs when a service provider produces so much incentive to continue acquiring its own products across its ecosystem that the consumer effectively becomes stuck buying the provider’s goods and services, lest he/she suffer either lackluster integrations or the switching cost of starting over with a new provider. Look no further than a comparison of Apple vs Microsoft vs Google products for examples of vendor lock-in at work.

不过，我会提请注意。 当服务提供商产生如此巨大的动机来继续在其整个生态系统中继续购买自己的产品时，就会发生被称为“供应商锁定”的现象，以至于消费者实际上陷入了购买提供商的商品和服务的困境，以免他/她遭受要么平淡无奇的整合或从新提供商那里重新开始的转换成本。 只需比较一下Apple，Microsoft和Google产品，就可以发现厂商锁定的例子。

The courses at ML University indeed appear at the outset to provide a lot of general applicability across the ML and software space. It is likely that 80–90% of all of its material will do so, which is great!

实际上，ML大学的课程确实从一开始就提供了在ML和软件领域的大量通用性。 它所有材料的80-90％可能会这样做，这太好了！

However, as you go through the courses, remain keen on staying up-to-date on how other providers are accomplishing similar products and services. To be a truly marketable ML practitioner in this evolving workforce, one must stay flexible in showing ML proficiency independent of algorithm, language, framework, and platform provider.

但是，在学习这些课程时，请始终热衷于了解其他提供商如何完成类似的产品和服务。 要在这支不断发展的员工队伍中成为真正可销售的ML练习者，必须保持灵活性，以独立于算法，语言，框架和平台提供者来展示ML熟练程度。

资源资源 (Resources)

• Github pages for the NLP, Computer Vision, and Tabular Data ML courses

  适用于NLP ， 计算机视觉和表格数据ML课程的Github页面

• ML University Youtube page

  ML University Youtube页面

• ML University announcement article

  ML大学发表文章

保持最新 (Stay Up To Date)

Aside from here on Medium, keep yourself updated with the LifeWithData blog and my Twitter. Through those platforms, I provide more long-form and short-form thoughts, respectively.

除了Medium，您还可以通过LifeWithData博客和Twitter保持自己的最新状态。 通过这些平台，我分别提供了更多的长篇和短篇思想。

If you’re not a fan of emails and social media, but still want to stay in the loop, consider adding lifewithdata.org/blog and lifewithdata.org/newsletter to a Feedly aggregation setup.

如果您不喜欢电子邮件和社交媒体，但仍然想保持联系，请考虑将lifewithdata.org/blog和lifewithdata.org/newsletter添加到Feedly聚合设置中。