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H2O Release 3.46
We are excited to announce the release of H2O-3 3.46.0.1! Some of the highlights of this major release are that we added custom metric support for XGBoost, allowed grid search models to be sorted with custom metrics, and we enabled H2O MOJO and POJO to work with MLFlow. Several improvements were also made to the Uplift model (like MLI ...
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H2O Release 3.44
We are excited to announce the release of H2O-3 3.44.0.1! We have added and improved many items. A few of our highlights are the implementation of AdaBoost, Shapley values support, Python 3.10 and 3.11 support, and added custom metric support for Deep Learning, Uplift Distributed Random Forest (DRF), Stacked Ensemble, and AutoML. Please r...
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Building a Fraud Detection Model with H2O AI Cloud
In a previous article [1], we discussed how machine learning could be harnessed to mitigate fraud. This time, we’ll delve into a step-by-step guide on leveraging H2O AI Cloud to construct efficient fraud detection models. We’ll tackle this process in three critical stages: build, operate, and detect. First, we’ll utilize Driverless AI in ...
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A Look at the UniformRobust Method for Histogram Type
Tree-based algorithms, especially Gradient Boosting Machines (GBM’s), are one of the most popular algorithms used. They often out-perform linear models and neural networks for tabular data since they used a boosted approach where each tree built works to fix the error of the previous tree. As the model trains, it is continuously self-corr...
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Reducing False Positives in Financial Transactions with AutoML
In an increasingly digital world, combating financial fraud is a high-stakes game. However, the systems we deploy to safeguard ourselves are raising too many false alarms, with over 90% of fraud alerts being false positives. These false positives, not only frustrating for consumers but also costly for financial institutions, can eclipse t...
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H2O.ai and Snowflake Enable Developers to Train, Deploy, and Score Containerized Software Without Compromising Data Security
H2O.ai today announced its participation as a launch partner for Snowflake’s Snowpark Container Services (available in private preview), which provides our joint customers with the flexibility to train, deploy, and score models all within their Snowflake account. This further expands the ease of use for data science teams to create machin...
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H2O Releases 3.40.0.1 and 3.42.0.1
Our new major releases of H2O are packed with new features and fixes! Some of the major highlights of these releases are the new Decision Tree algorithm, the added ability to grid over Infogram, an upgrade to the version of XGBoost and an improvement to its speed, the completion of the maximum likelihood dispersion parameter and its expan...
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10 Consejos para Convertirte en un Científico de Datos Exitoso
La ciencia de datos llegó para quedarse. Los científicos de datos utilizan sus habilidades para ayudar a las empresas a tomar mejores decisiones sobre sus productos, servicios, a optimizar procesos, ahorrar y mejorar rentabilidad. Convertirse en un científico de datos de éxito implica muchos aspectos y el estudio continuo, ya que es un...
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Explaining models built in H2O-3 — Part 1
Machine Learning explainability refers to understanding and interpreting the decisions and predictions made by a machine learning model. Explainability is crucial for ensuring the trustworthiness and transparency of machine learning models, particularly in high-stakes situations where the consequences of incorrect predictions can be signi...
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New in Wave 0.24.0
Another Wave release has arrived with quite a few exciting new features. Let’s quickly go over the biggest ones.Wave init CLIHow many times you wanted to build a Wave app fast, but then you realized you need to start from scratch, copy over the skeleton of your app and work up from there? For these exact reasons, we introduced a new wave...
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Bias and Debiasing
An important aspect of practicing machine learning in a responsible manner is understanding how models perform differently for different groups of people, for instance with different races, ages, or genders. Protected groups frequently have fewer instances in a training set, contributing to larger error rates for those groups. Some models...
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Data Science with H2O.ai: An Introduction to Machine Learning and Predictive Modeling
Our own Jonathan Farland recently recorded a talk about machine learning and predictive modeling. In his talk, Jon also gave an overview of open source H2O and H2O AI Cloud . This video is a great resource for getting up to speed with the latest technology from H2O in half an hour. Some of you may prefer to go through the slides while l...
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H2O Release 3.36 (Zorn)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release are Distributed Uplift Random Forest, an algorithm typically used in marketing and medicine to model uplift, and Infogram, a new research direction in machine learning that focuses on interpretability and fairness in...
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H2O.ai Tools for a Beginner
Note : this is a community blog post by Shamil Dilshan Prematunga . It was first published on Medium .Hey, this is not a deep technical blog. I’d like to share the experience I had with H2O tools when I was studying Machine Learning. As a Research Engineer, I am currently working on an area based on Telecommunication. Day by day with my e...
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New Features Now Available with the Latest Release of the H2O AI Cloud 21.10
The Makers here at H2O.ai have been busy building new features and enhancing capabilities across our AI platform . Designed to support our core mission of democratizing AI, these additions to our platform simplify the ability to make AI you can trust, operate it efficiently and innovate with ready-made AI applications.Launched in January ...
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Introducing DatatableTon - Python Datatable Tutorials & Exercises
Datatable is a python library for manipulating tabular data. It supports out-of-memory datasets, multi-threaded data processing and has a flexible API.If this reminds you of R’s data.table , you are spot on because Python’s datatable package is closely related to and inspired by the R library.The release of v1.0.0 was done on 1st July,...
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H2O Release 3.34 (Zizler)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve added Extended Isolation Forest for improved results on anomaly detection problems, and we’ve implemented the Type III SS test (ANOVAGLM) and the MAXR method to GLM. For existing algorithms, we improved the pe...
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Visualizing Large Datasets with H2O-3
Exploratory data analysis is one of the essential parts of any data processing pipeline. However, when the magnitude of data is high, these visualizations become vague. If we were to plot millions of data points, it would become impossible to discern individual data points from each other. The visualized output in such a case is pleasing ...
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AI-Driven Predictive Maintenance with H2O AI Cloud
According to a study conducted by Wall Street Journal , unplanned downtime costs industrial manufacturers an estimated $50 billion annually. Forty-two percent of this unplanned downtime can be attributed to equipment failure alone. These downtimes can cause unnecessary delays and, as a result, affect the business. A better and superior al...
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The Emergence of Automated Machine Learning in Industry
This post was originally published by K-Tech, Centre of Excellence for Data Science and AI, powered by NASSCOM. The link of the post can be found here. The concept of Automated Machine Learning has gained much traction recently. Automated Machine Le...
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How Much is My Property Worth?
Note : this is a guest blog post by Jaafar Almusaad .How Much is My Property Worth?This is the million-dollar question – both figuratively and literally. Traditionally, qualified property valuers are tasked to answer this question. It’s a lengthy and costly process, but more critically, it’s inconsistent and largely subjective. Mind you, ...
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Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal
It is impossible to deploy successful AI models without taking into account or analyzing the risk element involved. Model overfitting, perpetuating historical human bias, and data drift are some of the concerns that need to be taken care of before putting the models into production. At H2O.ai, explainability is an integral part of our ML ...
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Safer Sailing with AI
In the last week, the world watched as responders tried to free a cargo ship that had gone aground in the Suez Canal. This incident blocked traffic through a waterway that is critical for commerce. While the location was an unusual one, ship collisions, allisions , and groundings are not uncommon. With all the technology that mariners hav...
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H2O AI Cloud: Democratizing AI for Every Person and Every Organization
Harnessing AI’s true potential by enabling every employee, customer, and citizen with sophisticated AI technology and easy-to-use AI applications. Democratization is an essential step in the development of AI, and AutoML technologies lie at the heart of it. AutoML tools have played a pivotal role in transforming the way we consume an...
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H2O-3 Improvements from Two University Projects
In September 2019 H2O.ai became a silver partner of the Faculty of Informatics at Czech Technical University in Prague. The main goal of this partnership is to make connections between students and companies to prepare an environment where students can use their knowledge in practice and gain real-work experiences. In general, within th...
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New Improvements in H2O 3.32.0.2
There is a new minor release of H2O that introduces two useful improvements to our XGBoost integration: interaction constraints and feature interactions.Interaction ConstraintsFeature interaction constraints allow users to decide which variables are allowed to interact and which are not.Potential benefits: Better predictive performance...
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Introducing H2O Wave
For almost a decade, H2O.ai has worked to build open source and commercial products that are on the leading edge of innovation in machine learning, from AutoML to Explainable AI . We are thrilled to announce the release of what we believe to be the future of AI Applications: H2O Wave . Wave is an open source, lightweight Python developmen...
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Mitos e verdades sobre o AutoML
Todas as revoluções que tivemos até hoje, tanto as tecnológicas quanto industriais, possuem uma semelhança: elas estão ligadas à forma como os seres humanos lidam com as máquinas. Antes, os processos eram feitos de forma muito manual e, com o tempo, acabaram sofrendo uma evolução natural voltada para a automação. Com o aprendizado de máqu...
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H2O on Kubernetes using Helm
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 ...
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H2O Release 3.32 (Zermelo)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve added RuleFit — an interpretable machine learning algorithm , introduced a new toolbox for model explainability, made Target Encoding work for all classes of problems, and integrated it in our AutoML framewor...
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Combining the power of KNIME and H2O.ai in a single integrated workflow
KNIME and H2O.ai , the two data science pioneers known for their open source platforms, have partnered to further democratize AI. Our approaches are about being open, transparent, and pushing the leading edge of AI. We believe strongly that AI is not for the select few but for everyone. We are taking another step in democratizing AI by ...
Read moreThe Challenges and Benefits of AutoML
Machine Learning and Artificial Intelligence have revolutionized how organizations are utilizing their data. AutoML or Automatic Machine Learning automates and improves the end-to-end data science process. This includes everything from cleaning the data, engineering features, tuning the model, explaining the model, and deploying it into p...
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The Benefits of Budget Allocation with AI-driven Marketing Mix Models
Excerpt of the white paper: “The Latest in AI Technologies Reinvent Media and Marketing Analytics @ Allergan” Authors: Akhil Sood, Associate Director @ Marketing Sciences, Allergan Dr. Michael Proksch, Senior Director @ H2o.ai Vijay Raghavan, Associate Vice President @ Marketing Sciences, AllerganIntroductionThe call for accountability in...
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Exploring the Next Frontier of Automatic Machine Learning with H2O Driverless AI
At H2O.ai, it is our goal to democratize AI by bridging the gap between the State-of-the-Art (SOTA) in machine learning and a user-friendly, enterprise-ready platform. We have been working tirelessly to bring the SOTA from Kaggle competitions to our enterprise platform Driverless AI since its very first release. The growing list of Driver...
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Sparkling Water 3.30.0.3 is out
Sparkling Water is about making machine learning simple, speedy, and scalable with Apache Spark. This blog provides an overview of the following new features: No H2O Client on Spark Driver Speedups Automatic String conversion to Categoricals No H2O Client on Spark DriverPreviously, Sparkling Water always started worker nodes eith...
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Running H2O cluster on a Kubernetes cluster
H2O is an open-source, in-memory platform for distributed, scalable machine learning. A perfect match for deployment on a Kubernetes cluster, the very modern way of deploying, serving & scaling applications. With the major release 3.30.0.1, released in Q1 2020, H2O obtained first class Kubernetes support .This article explains how t...
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H2O Release 3.30 (Zahradnik)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced support for Generalized Additive Models, added an option to build many models in parallel on segments of your dataset, improved support for deploying on Kubernetes, upgraded XGBoost with newly added...
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Insights From the New 2020 Gartner Magic Quadrant For Cloud AI Developer Services
We are excited to be named a Visionary in the new Gartner Magic Quadrant for Cloud AI Developer Services (Feb 2020), and have been recognized for both our completeness of vision and ability to execute in the emerging market for cloud-hosted artificial intelligence (AI) services for application developers. This is the second Gartner MQ tha...
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AI & ML Platforms: My Fresh Look at H2O.ai Technology
2020: A new year, a new decade, and with that, I’m taking a new and deeper look at the technology H2O.ai offers for building AI and machine learning systems. I’ve been interested in H2O.ai since its early days as a company (it was 0xdata back then) in 2014. My involvement had been only peripheral, but now I’ve begun to work with this comp...
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Key Takeaways from the 2020 Gartner Magic Quadrant for Data Science and Machine Learning
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 Learn...
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Parallel Grid Search in H2O
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 ord...
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The Super Bowl and Data Science: Changing the NFL with the Power of Machine Learning
Super Bowl LIV came and went. The San Francisco 49ers vs the Kansas City Chiefs. Personally, being from the The Bay, I was rooting for the 49ers, but you can’t always get what you want. Whoever came out on top, though, we were all looking forward to a great game full of fantastic plays and the kind of gridiron tenacity where players lay i...
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Grandmaster Series: How a Passion for Numbers Turned This Mechanical Engineer into a Kaggle Grandmaster
In conversation with Sudalai Rajkumar: A Kaggle Double Grandmaster and a Data Scientist at H2O.aiIt is rightly said that one should never seek praise. Instead, let the effort speak for itself. One of the essential traits of successful people is to never brag about their success but instead keep learning along the way. In the data science ...
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H2O Release 3.28 (Yu)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced support for Hierarchical GLM, added an option to parallelize Grid Search, upgraded XGBoost with newly added features, and improved our AutoML framework. The release is named after Bin Yu .Hierarchi...
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Scalable AutoML in H2O
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 ...
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Importing, Inspecting, and Scoring With MOJO Models Inside H2O
Machine-learning models created with H2O may be exported in two basic ways: Binary format, Model Object, Optimized (MOJO). An H2 O model can be saved in a binary format, which is tied to the very specific version of H2 O it has been created with. There are multiple reasons for such a restriction. One of the important reasons is that...
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A Deep Dive into H2O’s AutoML
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 ...
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Make your own AI — Add Your Game to Auto-ML Models
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-i...
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New Innovations in Driverless AI
What’s new in Driverless AIWe’re super excited to announce the latest release of H2O Driverless AI . This is a major release with a ton of new features and functionality. Let’s quickly dig into all of that: Make Your Own AI with Recipes for Every Use Case: In the last year, Driverless AI introduced time-series and NLP recipes to meet the...
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Mitigating Bias in AI/ML Models with Disparate Impact Analysis
Everyone understands that the biggest plus of using AI/ML models is a better automation of day-to-day business decisions, personalized customer service, enhanced user experience, waste elimination, better ROI, etc. The common question that comes up often though is — How can we be sure that the AI/ML decisions are free from bias/discrimina...
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H2O Release 3.26 (Yau)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced the ability to define a Custom Loss Function in our GBM implementation, and we’ve extended the portfolio of our machine learning algorithms with the implementation of the SVM algorithm. The release...
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Custom Machine Learning Recipes: The ingredients for success
Last updated: 07/23/19Machine learning is akin to cooking in several ways. A perfect dish originates from a tried-and-tested recipe, has the right combination of ingredients, and is baked at just the right temperature. Successful AI solutions work on the same principle. One needs fresh and right quality ingredients in the form of data, ...
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Getting started with H2O using Flow
This blog was originally published on towardsdatascience: https://towardsdatascience.com/getting-started-with-h2o-using-flow-b560b5d969b8A look into H2O’s open-source UI for combining code execution, text, plots, and rich media in a single document. Data collection is easy. Decision making is hard. Today, we have access to a humungous...
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Toward AutoML for Regulated Industry with H2O Driverless AI
Predictive models in financial services must comply with a complex regime of regulations including the Equal Credit Opportunity Act (ECOA), the Fair Credit Reporting Act (FCRA), and the Federal Reserve’s S.R. 11-7 Guidance on Model Risk Management. Among many other requirements, these and other applicable regulations stipulate predictive ...
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An Overview of Python’s Datatable package
This blog originally appeared on Towardsdatascience.com “There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days”: Eric Schmidt If you are an R user, chances are that you have already been using the data.ta...
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H2O-3, Sparkling Water and Enterprise Steam Updates
We are excited to announce the new release of H2O Core, Sparkling Water and Enterprise Steam.Below are some of the new features we have added:H2O-3 Yates (3.24.0.1) – 3/31/2019Download at: http://h2o-release.s3.amazonaws.com/h2o/rel-yates/1/index.html Bug [PUBDEV-6159] – The AutoMLTest.java test suite now runs correctly on a local mach...
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H2O Release 3.24 (Yates)
There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced cross-version support for model import, added new features for model interpretation, provided much-improved support for reading data from Apache Hive, and included various algorithm and AutoML impr...
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Boosting your ROI with AutoML & Automatic Feature Engineering
If your business has started using AI/ML tools or just started to think about it, this blog is for you. Whether you are a data scientist, VP of data science or a line of a business owner, you are probably wondering how AI will impact your organization in various ways or why your current strategies are not working somehow. If you are not ...
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Key Takeaways from the Gartner Magic Quadrant For Data Science & Machine Learning
The Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Jan 2019) is out and H2O.ai has been named a Visionary. The Gartner MQ evaluates platforms that enable expert data scientists, citizen data scientists and application developers to create, deploy and manage their own advanced analytic models.H2O.ai Key Highlights...
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H2O New Year releases
There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release 3.22.0.3 . Immediately followed by a more major release (but still on 3.22 branch) codename Xu, named after mathematician Jinchao Xu , whose work is focused on deep neural networks, besides many other fields of research.Of course, th...
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New features in H2O 3.22
Xia Release (H2O 3.22)There’s a new major release of H2O and it’s packed with new features and fixes! Among the big new features in this release, we introduce Isolation Forest to our portfolio of machine learning algorithms and integrates the XGBoost algorithm into our AutoML framework. The release is named after Zhihong Xia .Isolation ...
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Anomaly Detection with Isolation Forests using H2O
IntroductionAnomaly detection is a common data science problem where the goal is to identify odd or suspicious observations, events, or items in our data that might be indicative of some issues in our data collection process (such as broken sensors, typos in collected forms, etc.) or unexpected events like security breaches, server failu...
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Launching the Academic Program … OR ... What Made My First Four Weeks at H2O.ai so Special!
We just launched the H2O.ai Academic Program at our sold-out H2O World London. With nearly 1000 people in attendance, we received the first online sign-up forms submitted by professors and students alike. This program will massively democratize AI in academia, increasing the number of AI-skilled graduates – with both technical and busine...
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Welcome H2O.ai's Driverless AI Community!
I am very excited to announce the formation of the inaugural community for H2O Driverless AI users. The Driverless AI Community is open for anyone looking to engage with other users as well as experts from H2O.ai’s Driverless AI, Driverless AI is an award-winning automatic machine learning platform that does “AI to do AI” to solve re...
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H2O for Inexperienced Users
Some background: I am a rising senior in highschool, and the summer of 2018, I interned at H2O.ai. With no ML experience beyond Andrew Ng’s Introduction to Machine Learning course on Coursera and a couple of his deep learning courses, I initially found myself slightly overwhelmed by the variety of new algorithms H2O has to offer in both ...
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The different flavors of AutoML
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software (e.g. H2O , scikit-learn , keras ). Although these tools have made it easy to train and evaluate ma...
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H2O’s AutoML in Spark
This blog post demonstrates how H2O’s powerful automatic machine learning can be used together with the Spark in Sparkling Water.We show the benefits of Spark & H2O integration, use Spark for data munging tasks and H2O for the modelling phase, where all these steps are wrapped inside a Spark Pipeline. The integration between Spark and...
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H2O-3 on FfDL: Bringing deep learning and machine learning closer together
This post originally appeared in the IBM Developer blog here. This post is co-authored by Animesh Singh, Nicholas Png, Tommy Li, and Vinod Iyengar. Deep learning frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Chainer have reduced the effort and skills needed to train and use deep learning models. But for AI developers and data ...
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H2O + Kubeflow/Kubernetes How-To
Today, we are introducing a walkthrough on how to deploy H2O 3 on Kubeflow. Kubeflow is an open source project led by Google that sits on top of the Kubernetes engine. It is designed to alleviate some of the more tedious tasks associated with machine learning. Kubeflow helps orchestrate deployment of apps through the full cycle of devel...
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Sparkling Water 2.2.10 is now available!
Hi Makers! There are several new features in the latest Sparkling Water. The major new addition is that we now publish Sparkling Water documentation as a website which is available here . This link is for Spark 2.2. We have also documented and fixed a few issues with LDAP on Sparkling Water. Exact steps are provided in the documentation...
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Congratulations - H2O is a leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms
Congratulations – Thanks to the support of our customer community over the past years, H2O.ai is a leader and one with the most completeness of vision in Gartner Magic Quadrant for Data Science and Machine Learning Platforms. It is an ecosystem we dedicated a good part of this decade to open up and spring. This is testimony to the incr...
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New features in H2O 3.18
Wolpert Release (H2O 3.18)There’s a new major release of H2O and it’s packed with new features and fixes! We named this release after David Wolpert , who is famous for inventing Stacking (aka Stacked Ensembles ). Stacking is a central component in H2O AutoML , so we’re very grateful for his contributions to machine learning! He is also fa...
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New versions of H2O-3 and Sparkling Water available
Dear H2O Community, #H2OWorld is on Monday and we can’t wait to see you there! We’ll also be live streaming the event starting at 9:25am PST. Explore the agenda here . Today we’re excited to share that new versions of H2O-3 and Sparkling Water are available. We invite you to download them here: http://www.h2o.ai/download/ H2O-3.16 – MO...
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Driverless AI Blog
In today’s market, there aren’t enough data scientists to satisfy the growing demand for people in the field. With many companies moving towards automating processes across their businesses (everything from HR to Marketing), companies are forced to compete for the best data science talent to meet their needs. A report by McKinsey says th...
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Scalable Automatic Machine Learning: Introducing H2O's AutoML
Prepared by: Erin LeDell, Navdeep Gill & Ray Peck In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts and experts...
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Stacked Ensembles and Word2Vec now available in H2O!
Prepared by: Erin LeDell and Navdeep Gill MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]} }); Stacked Ensembles ensemble <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, base_models = my_models) Python:ensemble = H2OStackedEnsembleEstimator(base_models=my_models) ensemble.train(x=x, y=y, training...
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What is new in Sparkling Water 2.0.3 Release?
This release has H2O core – 3.10.1.2Important Feature:This architectural change allows to connect to existing h2o cluster from sparkling water. This has a benefit that we are no longer affected by Spark killing it’s executors thus we should have more stable solution in environment with lots of h2o/spark node. We are working on article on ...
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What is new in H2O latest release 3.10.2.1 (Tutte) ?
Today we released H2O version 3.10.2.1 (Tutte). It’s available on our Downloads page, and release notes can be found here . Photo Credit: https://en.wikipedia.org/wiki/W._T._Tutte Top enhancements in this release: GLM MOJO Support: GLM now supports our smaller, faster, more efficient MOJO (Model ObJect, Optimized) format for model pu...
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