At H2O, we work really hard to make machine learning fast, accurate, and accessible to everyone. With H2O Driverless AI, users can leverage years of world-class, Kaggle Grand Masters experience and our GPU-accelerated algorithms (H2O4GPU ) to produce top quality predictive models in a fully automatic and timely fashion.
In our most recent release (version 1.1), we are going one step further to streamline the deployment process with MOJO (M odel O bJ ect, O ptimized). Inherited from our popular H2O-3 platform, MOJO is a highly optimized, low-latency scoring engine that is easily embeddable in any Java environment. With automatic pipeline generation in Driverless AI, users can go from automatic machine learning to production ready in just a few clicks. This blog post illustrates the usage of MOJO in Driverless AI with a simple example.
In a typical enterprise machine learning workflow, there are many things that could go wrong due to human errors, bad data science practices, different tools/infrastructure, incompatible code, lack of testing, versioning, communication and so on.
Driverless AI is our solution to ease those pain points in the second half of the workflow (i.e., creative feature engineering , model building, and deployment). We strongly believe that most organizations can benefit from automatic machine learning pipelines. A recent PayPal use-case shows that Driverless AI can help produce top quality predictive models with significant time and cost savings.
With Driverless AI, we are trying to mimic what top data science teams would do when they need to develop a new machine learning pipeline. Below are the four key areas of focus:
Like many other Driverless AI demos that you may have seen before at H2O World or our webinars, I am going to use the credit card dataset from the UCI machine learning repository for the MOJO example. Let me fast-forward the process to the end of a Driverless AI experiment and focus on the new MOJO options. From version 1.1.0, users have the option to build and download MOJO for fast, low-latency scoring. Here is a step-by-step walkthrough:
Click on DOWNLOAD MOJO SCORING PIPELINE to download mojo.zip . After unzipping the file, you should be able to see a new folder called mojo-pipeline . The pipeline.mojo and mojo2-runtime.jar in the folder are the two main files you need for the MOJO scoring pipeline.
Another key ingredient for MOJO pipeline is a valid Driverless AI license. You can download the license.sig file (usually in the license folder) from the machine hosting Driverless AI. Put the license file into the mojo-pipeline folder from the previous step.
The MOJO scoring pipeline requires Java 8 (or Java 7/8 from version 1.1.2). If you have not installed it, please follow the instructions here .
In the mojo-pipeline folder, you will find a small example.csv with some data samples. This dataset can be used for a quick test run. Open the folder in terminal and then run the following command:
Alternatively, run the full command like this:
java -Dai.h2o.mojos.runtime.license.file=license.sig -cp mojo2-runtime.jar ai.h2o.mojos.ExecuteMojo pipeline.mojo example.csv
It should return predictions (the probabilities of default payment in this credit card demo) and the time required for scoring each sample. Remember this scoring pipeline includes everything from complex feature transformations based on Kaggle Grand Masters’ recipes to computing predictions from the final model ensemble. With MOJO, our users have a low-latency scoring engine that can make new predictions in milliseconds .
Users can, of course, define and program their own scoring services. For more information, please go through the Compile and Run the MOJO from Java section in our Driverless AI documentation .
This blog post gives a quick overview of the automatic pipelines in Driverless AI. The key benefits for our users are:
Don’t take my words for it, sign up for a free 21-day trial and try Driverless AI yourself today .
Until next time,
Note #1 : Two years, numerous H2O models, slide decks, events and #360selfies later, I am finally making a return to blogging. I hope you enjoy reading this blog post.
Note #2 : H2O is going to Budapest again. Come find me, Erin, and Kuba at eRum conference from May 14 to 16. I will be delivering the “Automatic and Interpretable Machine Learning in R with H2O and LIME” workshop with a real, multimillion-dollar Moneyball Shiny app.