為什么和平精英無響應

重點 (Top highlight)

什么和為什么 (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.”

長期以來，亞馬遜一直在努力解決在機器學習和軟件工程領域精通個人的過剩需求(相對于供應)的問題。 迄今為止，他們已經開發了內部資源懶惰工具，以使員工快速掌握基本要素。 通常將其稱為OJT，用于“在職培訓”。

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只能走這么遠–員工人數龐大。 除了聘用的工人外，公司還依靠教育系統來定期向勞動力提供有能力的人才。 該系統已經運行了數百年。 但是，潮流正在轉變。 機器學習集成到行業工作流中的速度大大超過了教育系統提供設備齊全的人才的能力。 這部分是由于大型系統必須緩慢移動，也歸因于該領域主要算法和工具缺乏收斂性。 從根本上講，教育系統面臨的選擇是：過時地適應當前趨勢；堅持傳統技術；以及讓OJT解決最后一英里的問題。

亞馬遜的舉動 (Amazon’s Take)

Amazon has a great idea — meet halfway.

亞馬遜有一個好主意-半途而廢。

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.

學術機構將在很大程度上傾向于成熟的古典教育技術，這是正確的做法。 為了進一步解決最后一刻的OJT問題，而不是雇用后教育，亞馬遜現在從其內部的“ ML University”提供課程材料。 這樣，他們甚至可以在面試時間之前就培訓許多最終員工。 這有助于桌子的兩面。 準員工可以在申請工作之前學習更多相關材料，并感到他們在選擇工作和承諾方面更有能力。 另一方面，亞馬遜和類似公司可以比以往更直接地在面試中判斷人才。 由于公開提供了如此多的學習材料，因此當申請人在某個分區沒有經驗時，“懷疑的好處”的空間就更少了。

source]來源 ]

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.

僅發布了三門課程供立即使用：自然語言，計算機視覺和表格數據。 但是，隨著所有材料的公開發布，到2021年初，將會有更多的內容在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

“通過在課堂上公開亮相，我們為機器學習這一主題的科學界做出了貢獻，并使機器學習更加民主，” Werness補充說。 “該領域不僅限于具有高級科學學位或技術背景的個人。 這項使我們的課件上線的舉措代表了降低障礙的一步，這對希望開始實際機器學習的軟件開發人員，學生和其他構建者來說是一個障礙。”- 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“加速計算機視覺”課程的介紹。 整個課程可在類似的YouTube頁面上找到。

ML UniversityML大學計算機視覺課程簡介

意見和注意事項 (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.

這對于行業中機器學習的民主化非常重要。 長期以來，學術界非常開放，并與ML研究合作。 開源軟件運動也可以這樣說。 最近，在過去的十年左右的時間里，我們已經看到這些思想擴展到ML工業領域。 持續進行下去將確保經濟總量增長，同時仍可促進健康的競爭。

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.

不過，我會提請注意。 當服務提供商產生如此巨大的動機來繼續在其整個生態系統中繼續購買自己的產品時，就會發生被稱為“供應商鎖定”的現象，以至于消費者實際上陷入了購買提供商的商品和服務的困境，以免他/她遭受要么平淡無奇的整合或從新提供商那里重新開始的轉換成本。 只需比較一下Apple，Microsoft和Google產品，就可以發現廠商鎖定的例子。

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!

實際上，ML大學的課程確實從一開始就提供了在ML和軟件領域的大量通用性。 它所有材料的80-90％可能會這樣做，這太好了！

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，在學習這些課程時，請始終熱衷于了解其他提供商如何完成類似的產品和服務。 要在這支不斷發展的員工隊伍中成為真正可銷售的ML練習者，必須保持靈活性，以獨立于算法，語言，框架和平臺提供者來展示ML熟練程度。

資源資源 (Resources)

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

  適用于NLP ， 計算機視覺和表格數據ML課程的Github頁面

• ML University Youtube page

  ML University Youtube頁面

• ML University announcement article

  ML大學發表文章

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，您還可以通過LifeWithData博客和Twitter保持自己的最新狀態。 通過這些平臺，我分別提供了更多的長篇和短篇思想。

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.

如果您不喜歡電子郵件和社交媒體，但仍然想保持聯系，請考慮將lifewithdata.org/blog和lifewithdata.org/newsletter添加到Feedly聚合設置中。

翻譯自: https://towardsdatascience.com/amazon-wants-to-make-you-an-ml-practitioner-for-free-552c46cea9ba

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