5 Reasons to Learn H2O for High-Performance Machine Learning

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H2O is the scalable, open-source Machine Learning library that features . Here are 5 Reasons why it’s an essential library for creating production data science code.

This is part of a series of articles on essential Data Science and Web Application skills for 2020 and beyond:

Before I jump into H2O, let’s first understand the demand for ML. The 5-year trends in Technology Job Postings show a 440% increase in “Machine Learning” skills being requested, capturing a 7% share in all technology-related job postings.

Not just “Data Scientist” Jobs… ALL Technology Jobs.

We can safely say that if you are in a technology job (or seeking one) then you need to learn how to apply AI and Machine Learning to solve business problems.

The problem: There are a dozen machine learning and deep learning frameworks – , , , , , … These all take time and effort to learn. So, which framework should you learn for business?

Why I use and recommend H2O: H2O has singlehandedly produced results in hours that would have otherwise taken days or weeks. I recommend learning for applying Machine Learning to business data. I’ve been using H2O for several years now on both consulting projects and teaching it to clients. I have 5 reasons that explain how I have gotten this productivity enhancement using H2O on my business projects.

My Top 5-Reasons why I use and recommend learning .

automates the machine learning workflow, which includes automatic training and tuning of many models. This allows you to spend your time on more important tasks like feature engineering and understanding the problem.

In-memory processing with fast serialization between nodes and clusters to support massive datasets enables problems that traditionally need bigger tools to be solved in-memory on your local computer.

The result is 100x faster training than traditional ML.

H2O’s algorithms are developed from the ground up for distributed computing. The most popular algorithms are incorporated including:

I love using Docker (learn why) + to integrate models into Web Applications. H2O is built on (and depends on) Java, which traditionally creates overhead. But, with H2O Docker Images, it makes deploying H2O Models super easy with all necessary software inside the pre-built Docker Image.

can be integrated into Applications like this one – an Employee Attrition Prediction & Prevention App.

You need to learn H2O AutoML to build the Employee Attrition Shiny App. generates the “Employee Attrition Machine Learning Model” that scores the employees based on features like tenure, over time, stock option level, etc.

If you are ready to learn along with critical supporting technologies and data science workflow processes that follow an enterprise-grade system, then look no further: DS4B 201-R (Advanced Machine Learning & Business Consulting Course).

You follow a 10-week program for solving Business Problems with Data Science that teaches each of the tools needed to solve a $15M/year employee attrition problem using Machine Learning (), Explainable ML (), and Optimization ().

In weeks 5 & 6, you learn in-depth as part of your learning journey.

You are probably thinking, “How do I learn H2O if I have no Machine Learning background or coding experience?”

That’s why I created the 4-Course R-Track Program.

I look forward to providing you the best data science for business education.