That which is measured improves – Karl Pearson, Mathematician. Almost everyone has heard of accuracy, precision, and recall – the most common metrics for supervised learning. But not as many people know the metrics for unsupervised learning. So, in this article, we will take you through the most common methods and how to implement them. […]

Torch is a scalable and efficient deep learning framework. It offers flexibility and speed to build large scale applications. It also includes a wide range of libraries for developing speech, image, and video-based applications. The basic building block of Torch is called a tensor. All the operations defined in Torch use a tensor. Ok, let’s […]

In this article, I will show you how to build a Spotify Exploratory Data Analysis (EDA) app using H2O Wave from scratch. H2O Wave is an open-source Python development framework for interactive AI apps. You do not need to know Flask, HTML, CSS, etc. H2O Wave has ready-to-use user-interface components and charts, including dashboard templates, […]

There are three major branches of machine learning (ML): supervised, unsupervised, and reinforcement. Supervised learning makes up the bulk of the models businesses use, and reinforcement learning is behind front-page-news-AI such as AlphaGo. We believe unsupervised learning is the unsung hero of the three, and in this article, we break down four key ways you […]

Today, we are excited to announce integrations with Snowflake’s Snowpark and Java UDFs, Snowflake’s new developer experience. This essentially opens up access to Snowflake’s Data Marketplace to data scientists and developers, now allowing 3rd party unique datasets that can help improve model accuracy and deliver better business outcomes to be included in their often standard notebook based programming approaches.

Deploying real-world applications using bare YAML files to Kubernetes is a rather complex task, and H2O is no exception. As demonstrated in one of the previous blog posts. Greatly simplified, a cluster of H2O open source machine learning nodes is brought up in the following manner: A headless service to make initial node discovery and clustering […]

At H2O.ai we work with many enterprise customers, all the way from Fortune 500 giants to small startups. What we heard from all these customers as they embark on their data science and machine learning journey is the need to capture and manage more data cost-effectively, and the ability to share that data across their […]

In response to the wake of the pandemic called COVID-19, H2O.ai organized a panel discussion to cover AI in healthcare, and some best practices to put in place in order to achieve better outcomes. The attendees had many questions that we did not have the time to cover thoroughly throughout the course of that 1-hour […]

We are named a Visionary in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Feb 2020).  We have been positioned furthest to the right for completeness of vision among all the vendors evaluated in the quadrant. So let’s walk you through the key strengths of our machine learning platforms. Automatic Machine Learning […]

You have the data and now want to build a really really good AI/ML model and deliver to production. There are three options available today: Write the code yourself in a Jupyter notebook/R Studio etc., for training/validation and dev-ops model handoff. You decided to do the feature engineering also. Build your own features like above, […]