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Lessons learned leading AI teams

Happy International Women in Engineering Day! This day holds special meaning to me. As an Intuit Data Science Leader, as well as a Women in Data Science (WiDS) Tel Aviv ambassador (2018-2020), I appreciate the opportunity to share a bit about my personal journey, as well as the lessons I’ve learned leading artificial intelligence (AI) teams.
國際女性工程師日快樂!這一天對我來說有著特殊的意義。身為 Intuit 資料科學領導者以及特拉維夫女性資料科學 (WiDS) 大使(2018-2020 年),我很高興有機會分享一些我的個人經歷,以及我在領導過程中學到的經驗教訓人工智慧(AI) 團隊。

About me 關於我

Although I am an experienced data scientist and data science leader with an MSc in machine learning and signal processing, I started my career in the Israeli Air Force as a Flight Simulator instructor for Blackhawk and Super Stallion Helicopters. From there, I went on to complete my BSc and MSc in electrical engineering and computers. To pursue my interest in algorithms and signal processing, I focused my MSc research in a specific type of algorithms named – Machine Learning. From there I went to work in the industry in various algorithm development and data science lead roles. 

I spend my time leading artificial intelligence teams and co-hosting a data science and technology podcast, Unsupervised. I’m also an enthusiastic public speaker, co-founder of PyData Tel Aviv – a monthly meetup about different topics and applications of data science in python – and a technical blogger.
我花時間領導人工智慧團隊並共同主持數據科學和技術播客「Unsupervised」。我也是一位熱心的公共演講者、PyData Tel Aviv 的共同創辦人(每月一次的聚會,討論 Python 資料科學的不同主題和應用)以及一位技術部落客。

As you can probably tell, I’m passionate about science and technology. Working at Intuit has afforded me the privilege of developing ML models that have the power to affect people’s lives. Echoing Intuit’s call for integrity without compromise, my team and I bear the responsibility of holding our models to the highest standards.
正如您可能知道的那樣,我對科學和技術充滿熱情。在 Intuit 工作讓我有幸發展出能夠影響人們生活的機器學習模型。為了回應 Intuit 追求誠信、不妥協的號召,我和我的團隊有責任讓我們的模式達到最高標準。

Lessons learned leading AI teams

Working for a large technology organization, my AI team collaborates with other teams to deliver artificial intelligence solutions. An important part of our job is to recognize whether a problem is suitable for an AI solution. To recognize that, we start with looking at two things: (1) defining the customer problem and (2) ensuring there is relevant/sufficient data.
我的人工智慧團隊在一家大型技術組織工作,與其他團隊合作提供人工智慧解決方案。我們工作的一個重要部分是識別問題是否適合人工智慧解決方案。為了認識到這一點,我們首先關注兩件事:(1) 定義客戶問題;(2) 確保有相關/足夠的數據。

Starting with defining customer problems should come as no surprise to the Intuit Developer community. Intuit’s foundational concept of Design for Delight (D4D) is the gold standard. “Delighting” our customers begins with understanding who they are and what problem they’re facing (plus the emotion that is connected with that problem). Once we have this info, we then find the Ideal State, or the perfect solution. The team provides multiple ideas that are whittled down to the best one, which is tested by actual users.
對於 Intuit 開發人員社群來說,從定義客戶問題開始應該不足為奇。 Intuit 的「愉悅設計」(D4D) 基本概念是黃金標準。 「取悅」我們的客戶首先要了解他們是誰以及他們面臨什麼問題(以及與該問題相關的情緒)。一旦我們掌握了這些信息,我們就會找到理想狀態,或完美的解決方案。團隊提供了多種想法,然後將其縮減為最佳想法,並由實際用戶進行測試。

To define the customer problem, the AI team works with the requesting PM to fill the Intuit template. “<Customer> is trying to gain/do this <benefit> but is unable to/hindered because of <problem>. Intuit/Intuit AI will help deliver this <benefit> by <how improvement achieved>, which will lead to <improvement from value, to value>, which will be delivered by <delivery date> in support of <BB/IG/GD/TB>.”
為了定義客戶問題,AI 團隊與提出要求的 PM 合作填寫 Intuit 範本。 「<客戶>正在嘗試獲得/實現此<利益>,但由於<問題>而無法/受到阻礙。 Intuit/Intuit AI 將透過<如何實現改進>幫助實現這一<效益>,這將帶來<從價值到價值的改進>,這將在<交付日期>之前交付,以支援<BB/IG/GD /TB> 」。

Understanding the potential business impact and the relevant output metrics at this stage helps us prioritize the work in a data driven manner, which, in turn, enables us to focus our efforts in the most impactful areas. Note that some of the most impactful work we are doing comes from innovation of the team based on opportunities we find in the data, and it’s important for the AI team to keep the balance between requested projects and innovation projects.

Verifying that there is relevant, sufficient, and labeled data is the second step. Data is the key element in developing AI solutions. The whole idea in AI/ML is making the machine learn the model, which is the estimated relationship between variables from historical data. If we don’t have enough—or the right—data containing these relationships, we can’t learn.
第二步是驗證是否有相關的、充分的和標記的資料。數據是開發人工智慧解決方案的關鍵要素。 AI/ML 的整個想法是讓機器學習模型,也就是根據歷史資料估計變數之間的關係。如果我們沒有足夠的或正確的包含這些關係的數據,我們就無法學習。

After defining the customer problem and making sure relevant/sufficient data exist, we gather the mission-based team. In order to deliver an AI solution to production, a data scientist building AI is not enough. We need to understand better the domain of the problem, what action this AI solution would drive, and gather the right group of people to make this happen. This includes the PM to define the problem and to build the requirements for the right solution; a domain expert to help us gain deeper understanding the domain of the problem and what information could be relevant to solve it (could be a data analyst/PM/product developer); a MLE (Machine Learning Engineer) to work with the data scientist to deliver the model into production; a PD (Product Developer) to integrate the AI into the product and develop the action and user experience; a data expert to work with the data scientist on locating, understanding, and merging the data correctly; and a data analyst to analyze the model impact and to build the right dashboard for measuring performance.
在定義客戶問題並確保存在相關/足夠的數據後,我們聚集了基於任務的團隊。為了將人工智慧解決方案投入生產,僅僅建構人工智慧的資料科學家是不夠的。我們需要更了解問題的領域、該人工智慧解決方案將推動什麼行動,並聚集合適的人員來實現這一目標。這包括 PM 定義問題並建立正確解決方案的要求;領域專家幫助我們更深入了解問題的領域以及哪些資訊可能與解決問題相關(可以是資料分析師/PM/產品開發人員); MLE(機器學習工程師)與資料科學家合作將模型投入生產; PD(產品開發人員)將人工智慧整合到產品中並開發操作和用戶體驗;數據專家與數據科學家一起正確定位、理解和合併數據;數據分析師負責分析模型影響並建立正確的儀表板來衡量效能。

The mission-based team also has certain milestones/timelines that help them deliver the solution. They include the three steps already listed, plus:

  • Decide the type of model for the problem (supervised, unsupervised, semi-supervised), and understand the action the AI would drive and define the architecture (which pipes we need to connect for the model to consume the data and give the response in the right latency and integrate it into right flow).
  • Build initial AI solution, evaluate the performance based on validation data, and estimate effect on business metric.
  • Get PM feedback on solution and results.
    取得有關解決方案和結果的 PM 回饋。
  • Reiterate the model/data based on PM’s feedback .
    根據 PM 的回饋重申模型/數據。
  • Integrate the model to production in silent mode.
  • Monitor model health and performance in silent production, and evaluate the impact on business metrics.
  • A/B test (action on part of the population to evaluate impact in a controlled experiment).
    A/B 測試(對部分人群採取行動以評估受控實驗的影響)。
  • Release the model in production (monitor model health and performance in production using dashboards and alerts).

The last crucial step is measuring and monitoring model impact in production. This is how you make the most out of an AI project. We must understand which metric we are moving for the business and focus on measuring that outcome at all times, from research to production.

During these AI projects, I’ve learned that to achieve the optimal solution, the mission-based team should all be aligned around the customer problem, the metrics and the solution design. They should be meeting on a weekly basis and tracking the progress on all fronts.

My journey continues 我的旅程還在繼續

Ultimately, the lessons I’ve learned during my time as an Intuit team member (and as a WiDS ambassador) have provided even further lessons:
最終,我在擔任 Intuit 團隊成員(以及 WiDS 大使)期間學到的經驗教訓提供了更多的經驗教訓:

  1. Bring your whole self to work: Find ways to creatively use your wide variety of skills in the workplace. This would make you grow in multiple aspects and in general increase engagement levels for you and your team.
  2. Share your work publicly: The rigor required to deliver a public presentation would make you a better professional and drive useful dialogue with the community, which will open your mind and help you improve future solutions.
  3. Having friends in the industry: Learning and consulting with my fellow data science professionals across the industry had enabled me to bring best practices and top notch technology to my team, which had a significant contribution to my success as a data scientist and a data science leader. In addition, it enabled me to lead cross-industry data science initiatives.
  4. Take risks and say ‘yes’ to opportunities: The opportunities I took going out of my comfort zone have accelerated my career advancement and personal growth. These include starting a meetup group, a data science podcast, a WiDS conference in Israel, and taking on a leadership role at Intuit.
    敢於冒險並對機會說「是」:我走出舒適圈的機會加速了我的職業進步和個人成長。其中包括創辦一個聚會小組、一個數據科學播客、在以色列舉行的 WiDS 會議以及在 Intuit 擔任領導職務。

My journey so far has been heavily influenced by following these guidelines, and I hope they will help you as you forge your own path. And, if you’re creating unique apps for our Intuit customers, check out how artificial intelligence is redefining apps.
到目前為止,我的旅程深受遵循這些準則的影響,我希望它們能在您開闢自己的道路時為您提供幫助。而且,如果您正在為我們的 Intuit 客戶創建獨特的應用程序,請了解人工智慧如何重新定義應用程式。

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