Watch Make with H2O.ai recordings anytime.
Overview of H2O AI Cloud
The H2O AI Cloud is a state-of-the-art artificial intelligence (AI) cloud platform that enables data scientists, analysts, and developers to easily make, operate and innovate with AI in order to solve business problems. In this webinar, we'll cover each of our cloud products and why data scientists and analysts are using our AI Cloud to speed up experimentation, tackle deep learning programs, and remove operational constraints that take time away from making AI.
Read Maloney, SVP of Marketing
Vinod Inyegar, VP Products
Natural Language Processing (NLP) with H2O Hydrogen Torch
H2O Hydrogen Torch is democratizing AI, allowing all data scientists, from the novice to the expert, to build state-of-the-art deep learning models without code. It unlocks value from unstructured data to help teams understand it at scale and provides a powerful engine to solve complex problems in natural language processing (NLP), computer vision (CV) and audio analysis areas. In this webinar we will focus on NLP use cases. We'll provide an overview of H2O Hydrogen Torch, cover NLP use cases, and demonstrate how to rapidly build an NLP model.
Dmitry Gordeev, H2O.ai Kaggle Grandmaster
AI in Snowflake with H2O AI Cloud
The Snowflake Data Cloud can be used to train H2O.ai models as well as score models at scale. Using these two technologies enable organizations to use models directly within the warehouse, so that both users and applications have access to real time predictions. By unlocking the predictive power of the H2O.ai models, many new and valuable business cases become achievable.
Eric Gudgion, VP Field Enterprise Architecture
Accuracy Masterclass Part 1 - Choosing the “Right’ Metric for Success
Finding the "right" optimization metric for a data science problem may not be a straightforward task. One may expect that a “good” model would be able to get superior results versus all data science metrics available, however quite often this is not the case. This is a misconception. Therefore finding the metric most appropriate for the given objective is critical to building a scccessful data science model.
Marios Michailidis, Competitive Data Scientist
Getting Started with H2O Document AI
H2O Document AI makes highly accurate models by using a combination of Intelligent Character Recognition (ICR) and Natural Language Processing (NLP) to leverage learning algorithms for optical character recognition (OCR) and document layout recognition. In this webinar we'll cover an overview of H2O Document AI, demo the product and talk about successfull customer use cases.
Mark Landry, Kaggle Grandmaster
Accuracy Masterclass Part 2 - Validation Scheme Best Practices
Setting up a validation strategy is one of the most crucial steps in creating a machine learning model. A poorly designed validation scheme can lead to a major model accuracy overestimation or even completely erroneous model. There are common concepts of how to set up a validation strategy and what are the typical mistakes to avoid.
Dmitry Gordeev, Kaggle Grandmaster
Accuracy Masterclass Part 3 - Feature Selection Best Practices
Not all features are created equal. It is tempting to put all available features into the model but if you have too many features or features which are unrelated and/or noisy this could hurt model's performance. Finding best feature subset could be very useful in industrial application:
• it enables model to train faster
• reduces complexity of data pipeline
• reduces overfitting
• might improve performance.
Sometimes, less is better.
Dmitry Larko, Kaggle Grandmaster
Accuracy Masterclass Part 4 - Time Series Modeling
This webinar will dive deeper into workflows for time series modeling such as forecasting. We'll show how to make sure that temporal causality is preserved during modeling, how to automatically generated lag-based features, how to deal with trends, and how to measure the performance of the model as time advances.
Megan Kurka, Data Scientist
Accuracy Masterclass Part 5 - The Last Mile of Accuracy
AutoML tools help data scientists avoid common pitfalls and achieve their desired accuracy and interpretability. AutoML products are generally available in open-source and closed-source. We discuss how H2O Driverless AI stacks-up against other tools for accuracy and extensibility.
Jon McKinney, Director of Research
H2O Document AI Part 2
As covered in the first session (Getting Started with H2O Document AI), H2O Document AI makes highly accurate models by using a combination of Intelligent Character Recognition (ICR) and Natural Language Processing (NLP) to leverage learning algorithms for optical character recognition (OCR) and document layout recognition. This webinar will provide a deep dive on H2O Document AI, including use cases and a hands-on follow along lab. Prior to this session, we recommend viewing the on-demand recording from the Make with H2O.ai session: Getting Started with H2O Document AI.
Mark Landry, Data Scientist & Product Manager