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Why Companies Need to Think About MLOps
by Adam Murphy | December 14, 2021 H2O AI Cloud , MLOps

For years machine learning (ML) researchers have focused on building outstanding models and figuring out how to squeeze every last drop of performance from them. But many have realized that creating top-performing models doesn’t necessarily equate to having them deliver business value. Often the best models can be very complex and costly ...

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An Introduction to Time Series Modeling: Traditional Time Series Models and Their Limitations
by Adam Murphy | December 03, 2021 H2O AI Cloud , Time Series

In the first article in this series, we broke down the preprocessing and feature engineering techniques needed to build high-performing time series models. But we didn’t discuss the models themselves. In this article, we will dig into this. As a quick refresher, time series data has time on the x-axis and the value you are measuring (dema...

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Announcing the Fully Managed H2O AI Cloud
by Michelle Tanco | December 01, 2021 H2O AI Cloud

The H2O AI Cloud is the leading platform to make and access your own AI models and apps. Customers have had access to the H2O AI Hybrid Cloud for the last year, where they could manage the platform themselves on their favorite cloud or on-prem infrastructure. Today, we’re excited to announce a fully managed version of the H2O AI Cloud. Y...

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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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Amazon Redshift Integration for H2O.ai Model Scoring
by Eric Gudgion | November 22, 2021 Data Science , H2O AI Cloud

We consistently work with our partners on innovative ways to use models in production here at H2O.ai, and we are excited to demonstrate our AWS Redshift integration for model scoring. Amazon Redshift is a very popular data warehouse on AWS. We wanted to expand on the existing capacities of using data from Redshift to train a model on the ...

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Building Resilient Supply Chains with AI
by Adam Murphy | November 11, 2021 H2O AI Cloud

A global pandemic, a fundamental shift in the demand for goods and services worldwide, and the recent blockage of a major international trade route have all highlighted the need to build and maintain resilient supply chains.At the foundation of resilient supply chains lie accurate and reliable forecasts. The majority of traditional softwa...

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Introducing the H2O.ai Wildfire Challenge
by Jo-Fai Chow | November 05, 2021 AI4Good , H2O AI Cloud , H2O Hydrogen Torch

We are excited to announce our first AI competition for good – H2O.ai Wildfire Challenge .We’ve structured this challenge to be a global collaborative effort to do good for the world that we share. We want teams to submit their ideas and applications freely, knowing that other teams will learn from what they’ve done to improve their AI ap...

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MLB Player Digital Engagement Forecasting
by Jo-Fai Chow | October 29, 2021 Kaggle , Machine Learning

Are you a baseball fan? If so, you may notice that things are heating up right now as the Major League Baseball (MLB ) World Series between Houston Astros and Atlanta Braves tied at 1-1.MLB Postseason 2021 Results as of October 28 (source) This also reminded me of the MLB Player Digital Engagement Forecasting competition in which my coll...

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Announcing the H2O AI Feature Store
by Vinod Iyengar | October 28, 2021 H2O AI Cloud , Product Updates

We’re really excited to announce the H2O AI Feature Store – The only intelligent feature store in the market. We’ve been working on this for many months with our co-development partner: AT&T. This enabled us to build a first-of-its-kind platform that is designed to be enterprise-grade from day 1. It is built with best-of-breed techno...

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An Introduction to Time Series Modeling: Time Series Preprocessing and Feature Engineering
by Adam Murphy | October 26, 2021 Time Series

Time is the only nonrenewable resource – Sri Ambati, Founder and CEO, H2O.ai. Prediction is very difficult, especially if it’s about the future – Niels Bohr, Nobel Prize-Winning Physicist. Despite its inherent difficulty, every business needs to make predictions. You may want to forecast sales or estimate demand or gauge future inventory ...

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New Features Now Available with the Latest Release of the H2O AI Cloud 21.10
by H2O.ai Team | October 18, 2021 H2O AI Cloud , H2O Release

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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Time Series Forecasting Best Practices
by Jo-Fai Chow | October 15, 2021 H2O AI Cloud , Technical , Time Series

Earlier this year, my colleague Vishal Sharma gave a talk about time series forecasting best practices. The talk was well-received so we decided to turn it into a blog post. Below are some of the highlights from his talk. You can also follow the two software demos and try it yourself using our H2O AI Cloud .(Note : The video links with ...

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Improving NLP Model Performance with Context-Aware Feature Extraction
by Jo-Fai Chow | October 08, 2021 H2O AI Cloud , NLP , Technical

I would like to share with you a simple yet very effective trick to improve feature engineering for text analytics. After reading this article, you will be able to follow the exact steps and try it yourself using our H2O AI Cloud .First of all, let’s have a look at the off-the-shelf natural language processing (NLP) recipes in H2O Driver...

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Feature Transformation with the H2O AI Cloud
by Benjamin Cox | October 07, 2021 H2O AI Cloud

It is well known throughout the data science community that data preparation, pre-processing, and feature engineering are one of the most cumbersome parts of the data science workload. So as we continue to innovate here at H2O.ai with our end-to-end automated machine learning (autoML ) capabilities, we challenged ourselves to evolve the...

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Introducing DatatableTon - Python Datatable Tutorials & Exercises
by Rohan Rao | September 20, 2021 Datatable , H2O-3 , Python , Tutorials

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)
by Michal Kurka | September 15, 2021 H2O Release , H2O-3

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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From the game of Go to Kaggle: The story of a Kaggle Grandmaster from Taiwan
by Parul Pandey | September 13, 2021 Kaggle , Makers

In conversation with Kunhao Yeh: A Data Scientist and Kaggle Grandmaster In these series of interviews, I present the stories of established Data Scientists and Kaggle Grandmasters at H2O.ai, who share their journey, inspirations, and accomplishments. These interviews are intended to motivate and encourage others who want to understand...

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Visualizing Large Datasets with H2O-3
by Parul Pandey | September 09, 2021 H2O-3 , Tutorials

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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Innovation with the H2O AI Cloud
by H2O.ai Team | September 02, 2021 H2O AI Cloud

Consumer expectations for responsiveness, personalization, and overall efficiency have risen dramatically over the past several years as technology has become ubiquitous across both our personal and professional lives. These rapidly growing expectations demand an expansion in focus from simply solving narrow use cases with machine learnin...

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Interning with H2O.ai- Robie Gonzales
by H2O.ai Team | August 31, 2021 Community

This blog post is by Robie Gonzales, who has interned with us for the last 8 months. Thank you for your awesome work, Robie! When I started my internship eight months ago, I had minimal knowledge about machine learning and artificial intelligence. Over the course of these months, my experience as a Full Stack Developer has allowed me to ...

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AI-Driven Predictive Maintenance with H2O AI Cloud
by Parul Pandey, Asghar Ghorbani | August 02, 2021 AutoML , H2O AI Cloud , Machine Learning Interpretability , Manufacturing

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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What are we buying today?
by Rohan Rao | July 05, 2021 Community , H2O Hydrogen Torch

Note : this is a guest blog post by Shrinidhi Narasimhan .It’s 2021 and recommendation engines are everywhere. Be it online shopping, food, music, and even online dating, the race to provide personalized recommendations to the user has many contenders. The technology of giving users what they need based on their buying strategies or digit...

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The Emergence of Automated Machine Learning in Industry
by Parul Pandey | June 30, 2021 AutoML , Company

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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What does it take to win a Kaggle competition? Let's hear it from the winner himself.
by Parul Pandey | June 14, 2021 Data Science , Kaggle , Makers

In this series of interviews, I present the stories of established Data Scientists and Kaggle Grandmasters at H2O.ai, who share their journey, inspirations, and accomplishments. These interviews are intended to motivate and encourage others who want to understand what it takes to be a Kaggle Grandmaster. In this interview, I shall be ...

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H2O Integrates with Snowflake Snowpark/Java UDFs: How to better leverage the Snowflake Data Marketplace and deploy In-Database

One of the goals of machine learning is to find unknown predictive features, even hidden from subject matter experts, in datasets that might not be apparent before, and use those 3rd party features to increase the accuracy of the model.A traditional way of doing this was to try and scrape and scour distributed, stagnant data sources on th...

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Getting the best out of H2O.ai’s academic program
by Ana Visneski, Jo-Fai Chow | May 19, 2021 Academic Program

“H2O.ai provides impressively scalable implementations of many of the important machine learning tools in a user-friendly environment. Allowing for free academic use sets a generous example for commercial software developers — it is also the way forward in the era of open-source software.” – Professor Trevor J. Hastie, John A. Overdeck ...

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Regístrese para su prueba gratuita y podrá explorar H2O AI Cloud
by Ana Visneski, Jo-Fai Chow | May 17, 2021 H2O AI Cloud

Recientemente, lanzamos nuestra prueba gratuita de 14 días de H2O AI Cloud, lo que le brinda la oportunidad de obtener una experiencia práctica con nuestra plataforma más nueva de machine learning. H2O AI Cloud es una plataforma de inteligencia artificial de principio al fin que permite a las organizaciones crear, compartir y usar rápidam...

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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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Navegación más segura con Inteligencia Artificial
by Ana Visneski, Jo-Fai Chow, Kim Montgomery | May 10, 2021

El mes pasado, el mundo fue testigo de cómo socorristas intentaron liberar un buque de carga que había encallado en el Canal de Suez. Este incidente bloqueó el tráfico a través de una vía navegable que es esencial para el comercio. Aunque la ubicación fue inusual, las colisiones de buques, las colisiones de buques con objetos fijos y los...

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What it takes to become a World No 1 on Kaggle
by Parul Pandey | May 03, 2021 Data Science , Kaggle , Machine Learning , Makers

In conversation with Guanshuo Xu: A Data Scientist, Kaggle Competitions Grandmaster, and a Ph.D. in Electrical Engineering. In this series of interviews, I present the stories of established Data Scientists and Kaggle Grandmasters at H2O.ai , who share their journey, inspirations, and accomplishments. The intention behind these interviews...

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Unwrap Deep Neural Networks Using H2O Wave and Aletheia for Interpretability and Diagnostics

The use cases and the impact of machine learning can be observed clearly in almost every industry and in applications such as drug discovery and patient data analysis, fraud detection, customer engagement, and workflow optimization. The impact of leveraging AI is clear and understood by the business; however, AI systems are also seen as b...

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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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H2O.ai logra gran posicionamiento en integridad de visión en el cuadrante Visionarios del Cuadrante Mágico de Gartner 2021 para Data Science y Machine Learning
by Read Maloney | April 11, 2021 Business , Community , Gartner , H2O AI Cloud

En H2O.ai, nuestra misión es democratizar la IA y creemos que impulsar el valor de los datos es un esfuerzo de equipo. A menudo, los ingenieros de datos deben organizar y preparar los datos y luego los científicos de datos deben crear modelos. Los modelos, una vez creados, deben ponerse en producción y el personal de TI y de DevOps debe m...

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Safer Sailing with AI
by Ana Visneski, Jo-Fai Chow, Kim Montgomery | April 01, 2021 Customers , Data Science , H2O Hydrogen Torch , H2O-3 , Machine Learning Interpretability

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.ai é a mais avançada por sua capacidade de execução no quadrante dos visionários no relatório do Gartner de Ciências de Dados e Machine Learning em 2021
by Read Maloney | March 16, 2021 Business , Community , Gartner , H2O AI Cloud

*Este artigo foi originalmente escrito em inglês pelo SVP de Marketing, Read Maloney, e traduzido para português por Bruna Smith. Na H2O.ai, nossa missão é democratizar a Inteligência Artificial e acreditamos que o valor agregado, gerado a partir dos dados, é um trabalho em equipe. Os dados devem ser organizados e preparados, geralmente ...

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H2O.ai Placed Furthest in Completeness of Vision in 2021 Gartner Data Science and Machine Learning Magic Quadrant in the Visionaries Quadrant.
by Read Maloney | March 09, 2021 Business , Gartner , H2O Hydrogen Torch

At H2O.ai, our mission is to democratize AI, and we believe driving value from data is a team sport. Data needs to be organized and prepared, often by data engineers, and then models need to be built by data scientists. With models built, they need to be put into production and maintained by IT and DevOps personnel. Finally, these models...

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Learning from others is imperative to success on Kaggle says this Turkish GrandMaster
by Parul Pandey | February 15, 2021 Makers

In conversation with Fatih Öztürk: A Data Scientist and a Kaggle Competition Grandmaster. In this series of interviews, I present the stories of established Data Scientists and Kaggle Grandmasters at H2O.ai , who share their journey, inspirations, and accomplishments. These interviews are intended to motivate and encourage others who want...

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H2O-3 Improvements from Two University Projects
by Veronika Maurerova | February 08, 2021 Academic Program , H2O-3

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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Data to Production Ready Models to Business Apps in Just a Few Steps
by Shivam Bansal | February 05, 2021 H2O Hydrogen Torch , Solutions

Building a Credit Scoring Model and Business App using H2OIn the journey of a successful credit scoring implementation, multiple stakeholders and different personas are involved at different steps – Business Inputs, Dataset procurement, Data Analysis, Predictive Machine Learning, Data Storytelling, and Dashboarding. H2O.AI platforms such ...

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Using Python's datatable library seamlessly on Kaggle
by Parul Pandey, Rohan Rao | February 03, 2021 Data Munging , Data Science , Datatable

Managing large datasets on Kaggle without fearing about the out of memory error Datatable is a Python package for manipulating large dataframes. It has been created to provide big data support and enable high performance. This toolkit resembles pandas very closely but is more focused on speed.It supports out-of-memoy datasets, multi-thr...

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Successful AI: Which Comes First, the Data or the Question?
by Ellen Friedman | February 02, 2021 Business , H2O Driverless AI

Successful AI is a business process. Even the most sophisticated models, the latest algorithms, and highly experienced AI experts cannot make AI a practical success unless it is connected to a meaningful business goal . To make that happen, you need a good interaction between those with knowledge of the business and with the AI team. But ...

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Introducing H2O AI Cloud
by Benjamin Cox, Jo-Fai Chow | January 26, 2021 Cloud , H2O AI Cloud , Kubernetes

Organizations have made large investments in modernizing their data infrastructure and operations, but most still struggle to drive maximum value from their data. Many companies experimented with building large teams of expert data scientists, and while this approach did produce some valuable models, the cost was high and the timeframes ...

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Using AI to unearth the unconscious bias in job descriptions
by Parul Pandey, Shivam Bansal | January 19, 2021 H2O Hydrogen Torch , Responsible AI

“Diversity is the collective strength of any successful organization Unconscious Bias in Job DescriptionsUnconscious bias is a term that affects us all in one way or the other. It is defined as the prejudice or unsupported judgments in favor of or against one thing, person, or group as compared to another, in a way that is usually con...

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H2O Driverless AI 1.9.1: Continuing to Push the Boundaries for Responsible AI
by Benjamin Cox | January 18, 2021 H2O Driverless AI , Responsible AI

At H2O.ai, we have been busy. Not only do we have our most significant new software launch coming up (details here ), but we also are thrilled to announce the latest release of our flagship enterprise platform H2O Driverless AI 1.9.1. With that said, let’s jump into what is new: Faster Python scoring pipelines with embedded MOJOs for r...

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Meet the Data Scientist who just cannot stop winning on Kaggle.
by Parul Pandey | January 15, 2021 Kaggle

In conversation with Philipp Singer: A Data Scientist, Kaggle Double Grandmaster, and a Ph.D. in Computer Science. In this series of interviews, I present the stories of established Data Scientists and Kaggle Grandmasters at H2O.ai , who share their journey, inspirations, and accomplishments. These interviews are intended to motivate an...

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Liqui.do Speeds Credit Scoring for Fair Lending with H2O.ai
by Eve-Anne Trehin | January 12, 2021 Customers , Financial Services , Solutions

Liqui.do is a technological and innovative company developing a platform for leasing equipment for small and medium enterprises. As part of its business to provide a variety of credit options for companies that want to finance capital purchases, Liqui.do needs to rapidly and accurately assess the credit risk and scoring of a customer in o...

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