The H2O.ai Wildfire Challenge Winners Blog Series – Team PSR
May 31, 2022 AI4Good Community H2O Driverless AI WaveNote: this is a community blog post by Team PSR – one of the H2O.ai Wildfire Challenge winners. This blog represents an experience we gained by participating in the H2O wildfire challenge. We need to mention that competing in this challenge is like a journey in a knowledge pool. For a person who is willing […]
H2O.ai Tools for a Beginner
November 30, 2021 Beginners Community H2O H2O Driverless AI WaveNote: 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 […]
Introducing DatatableTon – Python Datatable Tutorials & Exercises
September 20, 2021 datatable Open Source Python TutorialsDatatable 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, 2021 and it’s probably […]
H2O Integrates with Snowflake Snowpark/Java UDFs: How to better leverage the Snowflake Data Marketplace and deploy In-Database
June 9, 2021 H2O AI Cloud H2O Driverless AI Partners Snowflake TechnicalToday, 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.
Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal
April 21, 2021 AutoML H2O Driverless AI Machine Learning Interpretability Uncategorized [EN]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 […]
H2O AI Cloud: Democratizing AI for Every Person and Every Organization
March 24, 2021 AutoML Data Science H2O AI Cloud H2O Driverless AI ModelOps WaveHarnessing 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 and understand data. Given […]
Using Python’s datatable library seamlessly on Kaggle
February 3, 2021 Data Munging Data Science datatableManaging large datasets on Kaggle without fearing about the out of memory error
Successful AI: Which Comes First, the Data or the Question?
February 2, 2021 Business H2O Driverless AISuccessful 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. […]
H2O Driverless AI 1.9.1: Continuing to Push the Boundaries for Responsible AI
January 18, 2021 H2O Driverless AI Responsible AIAt 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 […]
Automate your Model Documentation using H2O AutoDoc
November 19, 2020 Data Science H2O Driverless AICreate model documentation for Supervised learning models in H2O-3 and Scikit-Learn — in minutes. The Federal Reserve’s 2011 guidelines state that without adequate documentation, model risk assessment and management would be ineffective. A similar requirement is put forward today by many regulatory and corporate governance bodies. Thus model documentation today is more of a necessity than a choice. […]