H2O-3 is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, the machine learning algorithms are implemented. At H2O.ai, we design every operation, be it data transformation, training of machine learning models or even parsing to utilize the distributed computation model. In order to work with big data […]

Note: I’m grateful to Dr. Erin LeDell for the suggestions, corrections with the writeup. All of the images used here are from the talks’ slides. Erin Ledell’s talk was aimed at AutoML: Automated Machine Learning, broadly speaking, followed by an overview of H2O’s Open Source Project and the library. H2O AutoML provides an easy-to-use interface that automates […]

AI/ML Maturity Model Curve/Steps AI/ML Maturity models are published and updated periodically by a lot of vendors. The end goal is almost always about effecting transformation and automate processes in a short period and making AI the DNA/core of the business. One of the biggest challenges for businesses today is to clearly define what success […]

Machine-learning models created with H2O may be exported in two basic ways: Binary format, Model Object, Optimized (MOJO). An H2O model can be saved in a binary format, which is tied to the very specific version of H2O it has been created with. There are multiple reasons for such a restriction. One of the important reasons is […]

The demand for machine learning systems has soared over the past few years. This is majorly due to the success of Machine Learning techniques in a wide range of applications. AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to use machine learning models to address complex scenarios. […]

When Features and Algorithms compete, your Business Use Case(s) wins! H2O Driverless AI is an Automatic Feature Engineering/Machine Learning platform to build AI/ML models on tabular data. Driverless AI can build supervised learning models for Time Series forecasts, Regression, Classification, etc. It supports a myriad of built-in feature engineering transformers to work with numeric, categorical, […]

This is Part 2 of the blog post series and continuation of the original post, Predicting Failures from Sensor Data using AI/ML — Part 1. Missing Values & Data Imbalance One of the things to note is that the hard-disk data set has a lot of missing values across its columns. Check out the Missing Data Heat […]

This blog was written by Rohan Gupta and originally published here. 1. Introduction In today’s world, being a Data Scientist is not limited to those without technical knowledge. While it is recommended and sometimes important to know a little bit of code, you can get by with just intuitive knowledge. Especially if you’re on H2O’s […]

Last updated: 09/23/19 H2O Driverless AI provides a great new feature called “custom recipes”. These recipes are essentially custom snippets of code which can incorporate any machine learning algorithm, any scorer/metric and any feature transformer. A user can create custom recipes using python utilizing any external library or his/her own creations. This is a interesting feature […]

Introduction As part of my role within the automated machine learning space with H2O.AI and Driverless AI, I have seen that many times people struggle to find the right optimization metric for their data science problems. This process is even more challenging in regression problems where the errors are often not bounded like you normally have with probabilistic modeling. […]