Latest Musings from a Traveling Sales Engineer

Writers gonna write! February is shaping up to be a big traveling month for me. I was in Mountain View at the end of January for Sales Kick Off and then stayed for H2O World in San Francisco at the beginning of the month. Topping that off so far was a trip to Orlando for HIMSS last week. All incredible events but my traveling isn’t done yet! I plan to be all over the eastern seaboard in the coming week!

If you don’t innovate, you die.

The cool thing about being a part of Sales Kick Off is that we got to interact directly with the Makers. They pumped us for info on what we hear from the field, the enhancements and the ‘nice to have’ features in both Driverless AI and the Open Source H2O. A Customer to Maker feedback loop is critical for survival. It makes for a happy customer and it pushes us Makers to innovate. If you don’t innovate, you die.

Our open source software is what built our brand, it opened the door to customers.

On the surface it’s a strange thing to have two seemingly opposed products at the company you work for. Why should you pay for a commercial product if you already have a great set of open source algorithms and tools to use? That’s a great question and surprisingly, we don’t face very often. Our open source software is what built our brand, it opened the door to customers.

Yes, 1,ooo’s of organizations use our open source and probably never become Enterprise customers and a large percentage of them will never buy Driverless AI but that’s ok.

They know and trust our fast customer response teams.

It’s ok because they trust H2O to build and produce accurate and powerful models, for whatever they do. On the other hand, it’s the brand/reputation we built in Open Source that leads to Customers buying Driverless AI. They know and trust our fast customer response teams. They enjoy a reoccurring ‘touch point’ of a cadence they choose with our Customer Success team to see ‘how’s it going’ and help ‘get models into production.’

The best part? Driverless AI isn’t just our Open Source product slapped with a new GUI, it’s a product that’s built completely from the ground up. Just like I wrote here:

H2O.ai brings that clarity in the confusing AI space in a very simple way. Create world class algorithms, build an open source platform, and create an automated modeling platform. Our algorithms are used by over 14,000 organizations and by other software vendors ‘under the hood.’ We took all that knowledge and poured it into our automated modeling platform. We asked our Kaggle Grandmasters if you had the chance to build the best automated modeling platform in the world, what would it look like?

From ‘Finding Clarity in the Automated Modeling Space’ via H2O.ai blog

Driverless AI is built by Kaggle Grandmasters to solve highly complex data science problems. It has safeguards and processes in place to deal with Feature Generation, Feature Selection, Target Leakage, Transformations, Validation, Hyperparameter Tuning, etc. It’s really quite an impressive platform.

Driverless AI tends to innovate at what feels like the speed of light. Recently, a new customer asked when we would have a confusion matrix available in the GUI. As it stood at the time, the confusion matrix was exposed in the log file. Good but not great. I went back to the Makers, filed a ticket, and in about two weeks it was available in the GUI. We have a hyperactive and intense group of Makers that are DYING for feedback to keep building cool stuff.

Crazy right?

In all my time as a Sales Engineer (which isn’t that long), I’ve found that listening too and prioritizing a customer’s needs and wishes is what builds your brand and creates a thriving community. Making money from that is really just an affirmation of your brand. After all, what else do you want in your life?

Makers Gonna Make and Writers Gonna Write!

The Night before H2O World 2019

I’m in Mountain View this week for our annual Sales Kick-Off meeting and will be staying for H2O World this coming Monday and Tuesday. If our registration numbers are an indication, this H20 World will be our largest ever! As the ‘hip kids’ say, it’s going to be lit!

H20 World Schedule

The first day of H2O World is all about training. In London we had 500 people show up for training on the first day and almost twice that on the second day. I fully expect that number to be larger for San Francisco. There’s going to be breakout training for AutoML, Driverless AI, and much more. I’ll be stationed at the Driverless AI booth, so come and say hi!

I’ll be stationed at the Driverless AI booth, so come say hi!

The next day is all about presentations. I’m itching to see Tanya Berger-Wolf’s presentation “AI and Humans Combatting Extinction Together.” I’m also excited to see the “Women and Inclusion in Tech Panel” on Day 2. Then there’s Dmitry’s “Lessons Learned” presentation on Kaggle Airbus Ship Detection Challenge.

There are some people I want to finally meet at H2O World, like Rueben Diaz from Vision Banco and Leland Wilkenson. Rueben did a Driverless AI deployment for Vision Banco in Paraguay and I want to hear how he did on on IBM’s Power 9 box. I also want to finally meet Leland in person. I usually hear him on the phone during company meetings. Leland is the leader of Driverless AI’s AutoViz development and has a deep knowledge of visualizations and is the author of ‘The Grammar of Graphics.’

There’s always so many presentations, ideas, awesome coffee talk, that two days are never enough. All in all, I expect this to be a fun but intense few days. There’s always such an amazing vibe when you go to H2O world, the ideas, the problem solving. It feels like changing the world for the better is possible.

Full H2O World schedule and speaker list here. Hope to see you there!

Automatic Feature Engineering with Driverless AI

Dmitry Larko, Kaggle Grandmaster, and Senior Data Scientist at H2O.ai goes into depth on how to apply feature engineering in general and in Driverless AI. This video is over a year old and the version of Driverless AI shown is in beta form. The current version is much more developed today.

This is by far one of the best videos I’ve seen on the topic of feature engineering, not because I work for H2O.ai, but because it approaches the concepts in an easy to understand manner. Plus Dmitry does an awesome job of helping watchers understand with great examples.

The question and answer part is also very good, especially the discussion on overfitting. My notes from the video are below.

  • Feature engineering is extremely important in model building
  • “Coming up with features is difficult, time-consuming, requires expert knowledge. “Applied machine learning” is basically feature engineering” – Andrew Ng
  • Common Machine Learning workflow (see image below)
Feature Engineering, Driverless AI
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  • What is feature engineering? Example uses Polar coordinate conversions for linear classifications
  • Creating a target variable is NOT feature engineering
  • Removing duplicates/Missing values/Scaling/Normalization/Feature Selection IS NOT feature engineering
  • Feature Selection should be done AFTER feature engineering
  • Feature Engineering Cycle: Dataset > Hypotheis Set > Validate Hypothesis > Apply Hypothesis > Dataset
  • Domain knowledge is key, so is prior experience
  • EDA / ML model feedback is important
  • Validation set: use cross validation. Be aware of data leakage
  • Target encoding is powerful but can introduce leakage when applied wrong
  • Feature engineering is hard and very very time consuming
  • Feature engineering makes your model better, simpler models
  • Transform predictor/response variables into a normal distribution in some situation like log transform
  • Feature Encoding turns categorical features into numerical features
  • Labeled encoding and one hot encoding
  • Labeled encoding is bad, it implies an order which is not preferred
  • One hot encoding transforms into binary (dummy coding)
  • One hot encoding create a very sparse data set
  • Columns BLOW UP in size with one hot encoding
  • You can do frequency encoding instead of one hot encoding
  • Frequency Encoding is robust but what about balanced data sets?
  • Then you do Target Mean encoding. Downfall is high cardinality features. This can cause leakage!
  • To avoid leakage, you can use ‘leave one out’ schema
  • Apply Bayesian smoothing, calc a weight average on the mean of the training set
  • What about numerical features? Feature encoding using: Binning with quantiles / PCA and SVD / Clustering
  • Great, then how do you find feature interactions?
  • Apply domain knowledge / Apply genetic programming / ML also behavior (investigate model weights, etc)
  • You could encode categories features by stats (std dev, etc)
  • Feature Extraction is the application of extracting value out of hidden features, like zip code
  • Zip code can give you state and city information
  • You can extract day, week, holiday, etc can be extracted date-times

Update: The H2O.ai documentation on the feature transformations applied is here. Check it out, it’s pretty intense.