Today, I continue my adventure in autoML tools. One of the leaders is H2O’s Driverless AI offering. It has some great features that impressed me. One drawback I had in this evaluation was that I didn’t have enough time to train the ‘Watson’ dataset properly. While the results are unavailable, I have been able to share enough of the experience for you to get a feel for the tool.

今天，我繼續使用autoML工具進行冒險。 H2O的無人駕駛AI產品是領導者之一。 它的一些出色功能令我印象深刻。 我在此評估中的一個缺點是我沒有足夠的時間正確地訓練“ Watson”資料集。 雖然無法獲得結果，但我已經能夠與您分享足夠的經驗，以使您對該工具有所瞭解。

為什麼要使用無人駕駛AI？ (Why Driverless AI?)

I’ve seen several demos of Driverless AI over the past couple of years. I’ve done an evaluation myself using a trial in my AWS account in 2018. I know from experience and reputation; this is one of the top autoML tools available. They tout their visualizations, and I was impressed with them two years ago.

在過去的幾年中，我已經看過無人駕駛AI的多個演示。 我已經在2018年使用自己的AWS賬戶中的試用版進行了評估。 這是可用的頂級autoML工具之一。 他們吹捧他們的視覺化效果，兩年前，我對他們印象深刻。

設定和費用 (The setup and cost)

Like DataRobot, Driverless AI is a licensed product. Since last year, Driverless AI is available through IBM as well as other cloud platforms. I am unable to find the exact license cost for one named user for 2020. Based on my research, the price appears to be comparable to the DataRobot cost of $80k per year.

與DataRobot一樣，無人駕駛AI也是許可產品 。 自去年以來，可通過IBM以及其他雲平臺使用無人駕駛AI。 我無法找到一位指定使用者在2020年的確切許可成本。根據我的研究，該價格似乎與DataRobot每年8萬美元的成本相當。

Lucky for us, we can do a free trial of Driverless AI. There are a couple of options. You can get 14 days on a cloud implementation or 2 hours of a hosted platform. In the past, I’ve used the AWS Marketplace offering. For this demo, I’m using the hosted platform. Two hours is a short period, so I need to be efficient.

幸運的是，我們可以免費試用無人駕駛AI。 有兩種選擇。 您可以在雲實施上花費14天，在託管平臺上花費2個小時。 過去，我曾使用過AWS Marketplace產品。 對於此演示，我正在使用託管平臺。 兩個小時很短，所以我需要提高效率。

I set up an account on Aquarium. There are several labs available to work through. As suggested, I started the test drive lab. I was not initially sure if I could load my dataset. I was happy to see I could.

我在水族館開設了一個帳戶。 有幾個實驗室可以解決。 按照建議，我開始了測試驅動器實驗室。 最初我不確定是否可以載入我的資料集。 我很高興看到自己能做到。

Once the lab spun up, I got a URL that pointed to an AWS instance. Quick and easy.

實驗室啟動後，我得到了一個指向AWS例項的URL。 快捷方便。

資料 (The Data)

To keep parity across the tools in this series, I will stick to the Kaggle training file. Contradictory, My Dear Watson. Detecting contradiction and entailment in the multilingual text using TPUs. In this Getting Started Competition, we’re classifying pairs of sentences (consisting of a premise and a hypothesis) into three categories — entailment, contradiction, or neutral.

為了使本系列中的工具保持一致，我將堅持使用Kaggle培訓檔案。 矛盾的，親愛的沃森。 使用TPU檢測多語言文字中的矛盾和牽連 。 在本入門競賽中，我們將成對的句子(由前提和假設組成)分為三類-蘊涵，矛盾或中立。

6 Columns x 13k+ rows — Stanford NLP documentation

6列x 13k +行— Stanford NLP 文件

• id
  ID
• premise
  前提
• hypothesis
  假設
• lang_abv
  lang_abv
• language
  語言
• label
  標籤

載入資料 (Loading the data)

Loading the data is super easy, and some very nice visualizations are available before any training takes place. You can also add graphs to the list, so that’s a nice feature. Most autoML tools aren’t going to give you correlation graphs and radar plots. I thought the data heatmap was a bit weak, but that appears to have been due to the variety in the data (text). Interestingly when you download the visualizations, they arrive in .svg format. Nice if you have an Adobe license.

載入資料非常容易，在進行任何培訓之前，可以使用一些非常漂亮的視覺化檔案。 您還可以將圖形新增到列表中，因此這是一個不錯的功能。 大多數autoML工具不會為您提供相關圖和雷達圖。 我以為資料熱圖有些薄弱，但這似乎是由於資料(文字)的多樣性所致。 有趣的是，當您下載視覺化檔案時，它們以.svg格式到達。 如果您擁有Adobe許可證，那就很好。

訓練模型 (Training your model)

It’s pretty simple to get the models training. Launch Experiment. You can tune the Accuracy, Time, and Interpretability dials to what your preferences are. There are also MANY expert settings you can review.

進行模型訓練非常簡單。 啟動實驗。 您可以將“精度”，“時間”和“可解釋性”撥盤調整到您的首選項。 您還可以檢視許多專家設定。

Between this main visualization and the logs, you can get an excellent idea of the progress your training job is making. I appreciate this! I may have been too aggressive with my dials, and I was not able to fully make it through an entire experiment during my time allotment.

在此主要視覺化和日誌之間，您可以很好地瞭解培訓工作的進度。 我很欣賞這個！ 我的錶盤可能太激進了，在分配時間的過程中，我無法完全通過整個實驗來做到這一點。

評估培訓結果 (Evaluate Training Results)

Well, I didn’t have enough time to train the models with the time I had. I tried to start a new lab and try again, but I just got a ‘waiting for worker’ message for 45 minutes. I would have been interested in taking a look at the generated features. Feature generation is one of the noted differentiators of this tool.

好吧，我沒有足夠的時間來訓練模型。 我嘗試開始一個新實驗室，然後再試一次，但是我收到了45分鐘的“等待工人”訊息。 我本來想對生成的功能感興趣。 特徵生成是該工具的顯著差異之一。

The good news is that there are pre-run projects that you can poke through on your own.

好訊息是，您可以自己瀏覽一些預執行專案。

I did see H2O added an AutoReport feature! Very nice addition. The report isn’t quite as extensive as DataRobot’s, but it’s good work in the right direction.

我確實看到H2O添加了AutoReport功能！ 非常好。 該報告並不像DataRobot的報告那樣廣泛，但是在正確的方向上是一項很好的工作。

H2O has a free booklet on model interpretability that I highly recommend. They are a leader in this area.

我強烈建議H2O提供有關模型可解釋性的免費手冊。 他們是這一領域的領導者。

When you click Interpret this Model, you have to wait while the process runs. Don’t stare at the screen or you’ll find your eyes circling with the status’.

單擊“解釋此模型”時，必須等待過程執行。 不要凝視螢幕，否則您會發現眼睛在盤旋狀態。

Slowly the explanations become available. The result is worth the wait.

慢慢地可以得到解釋。 結果值得期待。

結論 (Conclusions)

Driverless AI is a great tool. They provide interesting visualizations and allow you to add additional ones. The model interpretability metrics and graphs are terrific. As with DataRobot, you pay for greatness.

無人駕駛AI是一個很棒的工具。 它們提供了有趣的視覺化效果，並允許您新增其他視覺化效果。 模型的可解釋性指標和圖表非常棒。 與DataRobot一樣，您需要付出巨大的代價。

The labs are free, so I encourage you to try them today. The pre-trained projects give you a good jump start so that you can take a look for yourself.

實驗室是免費的，所以我建議您立即嘗試。 經過預培訓的專案為您提供了一個良好的入門指南，以便您可以自己看看。

If you missed one of the articles in the series, here are the links.

如果您錯過了該系列的文章之一，請點選以下連結。