- How to Speed Up XGBoost Model Training - Dec 20, 2021.
XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.
- Make Pandas 3 Times Faster with PyPolars - May 31, 2021.
Learn how to speed up your Pandas workflow using the PyPolars library.
- A Rising Library Beating Pandas in Performance - Dec 11, 2020.
This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.
- KDnuggets™ News 20:n37, Sep 30: Introduction to Time Series Analysis in Python; How To Improve Machine Learning Model Accuracy - Sep 30, 2020.
Learn how to work with time series in Python; Tips for improving Machine Learning model accuracy from 80% to over 90%; Geographical Plots with Python; Best methods for making Python programs blazingly fast; Read a complete guide to PyTorch; KDD Best Paper Awards and more.
- Performance Testing on Big Data Applications - Aug 21, 2020.
You can use performance testing in any application you’re working on but it’s especially useful for big data applications. Let’s see why.
- Lincoln Clean Energy: Director, Asset Performance [Austin, TX] - Aug 19, 2019.
Seeking an Asset Performance Director, a role which requires an individual that possesses a strong technical skill set and the ability to communicate findings effectively throughout the organization.
- 9 Tips For Training Lightning-Fast Neural Networks In Pytorch - Aug 9, 2019.
Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.
- XLNet Outperforms BERT on Several NLP Tasks - Jul 1, 2019.
XLNet is a new pretraining method for NLP that achieves state-of-the-art results on several NLP tasks.
- The Four Levels of Analytics Maturity - Mar 26, 2019.
We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.
- Deep Learning Performance Cheat Sheet - Nov 8, 2018.
We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.
- Unleash a faster Python on your data - Mar 1, 2018.
Get real performance results and download the free Intel Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more.
- How to Make Your Database 200x Faster Without Having to Pay More - Nov 22, 2016.
Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.
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- Equifax: Strategic Data Performance Analyst - Oct 14, 2016.
Seeking a Strategic Data Performance Analyst, responsible for leading analytic initiatives pertaining to maximizing the effectiveness of EWS data assets, including: Data Quality Analysis and Improvement, Data Valuation Analytics, Data Usage Analytics, and Customer Profiling.
- The Big ‘Big Data’ Question: Hadoop or Spark? - Aug 5, 2015.
With a considerable number of similarities, Hadoop and Spark are often wrongly considered as the same. Bernard carefully explains the differences between the two and how to choose the right one (or both) for your business needs.
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- Interview: Andrew Duguay, Prevedere on Economic Intelligence from Integrating Public Datasets - Jul 30, 2015.
We discuss Analytics at Prevedere Software, understanding the impact of external factors on a company’s performance, features of in-memory correlation engine and economic intelligence by Prevedere.
- KDnuggets Interview: Amr Awadallah, CTO & Co-founder, Cloudera on the Future of Information Architecture Design - Jun 29, 2015.
We discuss Cloudera’s achievements, story behind the name ‘Cloudera’, CTO role, and key attributes of information architecture designed for future.
- Interview: James Taylor, Salesforce on Apache Phoenix – RDBMS for Big Data - Jun 5, 2015.
We discuss the beginning of Phoenix project, decision of making it open source, relational database layer on HBase, and key reasons for the superior performance of Apache Phoenix.
- Interview: Antonio Magnaghi, TicketMaster on Unifying Heterogeneous Analytics through Lambda Architecture - May 18, 2015.
We discuss the role of Data Science team at Ticketmaster, ecommerce data characteristics, analytics based on highly variant data flow, infrastructure challenges, and merits of lambda architecture.
- Interview: Bill Moreau, USOC on Empowering World’s Best Athletes through Analytics - Mar 26, 2015.
We discuss how United States Olympic Committee uses Big Data, how athletes respond to Analytical insights, integration of sports medicine into sports performance and sports injury.
- Interview: Paul Robbins, STATS on the Potential and Challenges for Sports Analytics - Jan 5, 2015.
We discuss Analytics at STATS, typical daily tasks, ICE Analytics platform, key challenges, response from coaches/players, career advice and more.
- RootMetrics: Statistical Analyst - Mar 14, 2014.
Join us at RootMetrics where we are providing an accurate view of wireless carrier and connected device performance, and help build a movement for an open mobile market that democratizes mobile performance data.