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Holden Karau

Holden Karau

Co-founder · Fight Health Insurance

9 activities
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Holden Ackerman

author

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Steve Holden

Senior Vice President and Head of Single-Family Analytics · Fannie Mae

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Activities & events

Title & Speakers Event
Holden Karau - Fight Health Insurance 2024-12-16 · 12:33
Holden Karau – guest , Joe Reis – founder @ Ternary Data

Health insurance denials are a big topic in America right now. Holden Karau joins me to talk about using AI to appeal health claim insurance denials.

Fight Health Insurance: https://fighthealthinsurance.com/

AI/ML
The Joe Reis Show
Listen
Event O'Reilly Data Science Books 2023-07-26
Scaling Python with Dask 2023-07-26
Mika Kimmins – author , Holden Karau – author

Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn. Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA. With this book, you'll learn: What Dask is, where you can use it, and how it compares with other tools How to use Dask for batch data parallel processing Key distributed system concepts for working with Dask Methods for using Dask with higher-level APIs and building blocks How to work with integrated libraries such as scikit-learn, pandas, and PyTorch How to use Dask with GPUs

data data-science data-science-tools dask API Cloud Computing NumPy Pandas Python PyTorch Scikit-learn
Scaling Python with Ray 2022-11-29
Boris Lublinsky – author , Holden Karau – author

Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to: Implement stateful applications with Ray actors Build workflow management in Ray Use Ray as a unified system for batch and stream processing Apply advanced data processing with Ray Build microservices with Ray Implement reliable Ray applications

data data-science Python
High Performance Spark 2017-05-25
Rachel Warren – author , Holden Karau – author

Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages

data data-engineering apache-spark AI/ML Scala Spark SQL Data Streaming
O'Reilly Data Engineering Books
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