python

A hands-on data science crash course for modeling and predicting the behavior of (large) distributed systems

A hands-on data science crash course for modeling and predicting the behavior of (large) distributed systems

December 17, 2019

Data science is a hot topic. Bart De Vylder offers a practical introduction that goes beyond the hype, exploring data analysis, visualization, and machine-learning techniques using Python for modeling the behavior of distributed systems. You'll leave with a solid starting point to implement data science techniques in your infrastructure or domain of interest.
Bringing People Together with Open Source

Bringing People Together with Open Source

December 17, 2019

ROSE (short for Red Hat Open Source for Education) is a cross-community effort lead by Red Hat Israel that brings students from Tira and Ra'anana to the Red Hat offices to learn about the Linux operat …
Predictive system performance data analysis (sponsored by Salesforce)

Predictive system performance data analysis (sponsored by Salesforce)

December 14, 2019

Jasmin Nakic and Samir Pilipovic examine the application of a linear regression predictive model on time series performance data, discussing and evaluating different models to find the optimal choice for a given dataset. All steps will be supported with Python-based scripts so that you can easily implement similar models on your own data.
TensorFlow and deep learning (without a PhD)

TensorFlow and deep learning (without a PhD)

December 6, 2019

With TensorFlow, deep machine learning has transitioned from an area of research into mainstream software engineering. Martin Grner walks you through building and training a neural network that recognizes handwritten digits with >99% accuracy using Python and TensorFlow.
The Elements of Kubernetes - Foundational Concepts for Apps Running on Kubernetes [I]

The Elements of Kubernetes - Foundational Concepts for Apps Running on Kubernetes [I]

December 6, 2019

The Elements of Style is one of the most important and foundational guidelines on how to write well. It has effectively summarized, in a list of seminal guidelines, how to harness the power of the E …
Automated data exploration: Building efficient analysis pipelines with dask

Automated data exploration: Building efficient analysis pipelines with dask

December 5, 2019

Data exploration usually entails making endless one-use exploratory plots. Victor Zabalza shares a Python package based on dask execution graphs and interactive visualization in Jupyter widgets built to overcome this drudge work. Victor offers an overview of the tool and explains how it was built and why it will become essential in the first steps of every data science project.
Modern Big Data Pipelines over Kubernetes [I]

Modern Big Data Pipelines over Kubernetes [I]

December 3, 2019

Big data used to be synonymous with Hadoop, but our ecosystem has evolved over time with new database, streaming and machine learning solutions which dont necessarily benefit from the Hadoop deployme …
Spark and R with sparklyr

Spark and R with sparklyr

November 30, 2019

R is a top contender for statistics and machine learning, but Spark has emerged as the leader for in-memory distributed data analysis. Douglas Ashton, Aimee Gott, and Mark Sellors introduce Spark, cover data manipulation with Spark as a backend to dplyr and machine learning via MLlib, and explore RStudio's sparklyr package, giving you the power of Spark without having to leave your R session.
Building a real-time recommendation engine with Neo4j

Building a real-time recommendation engine with Neo4j

November 27, 2019

William Lyon explains how to use a graph database to generate real-time recommendations using real-world data. William introduces graph data modeling and querying concepts using Neo4j and Cypher, the query language for graphs to import and query data, before demonstrating how to apply graph algorithms and NLP using Python data science tools to enhance your recommendations.
Databases and Docker: A survival guide

Databases and Docker: A survival guide

November 26, 2019

Containers are great ephemeral vessels for your applications. But what about the data that drives your business? It must survive containers coming and going, maintain its availability and reliability, and grow when you need it. Alvin Richards does some live coding to show key strategies to help you survive the transition to production.
Developer on the rise: Blurring the line between developer and data scientist with PixieDust

Developer on the rise: Blurring the line between developer and data scientist with PixieDust

November 26, 2019

Ready to dip your toe into data science? Va Barbosa explains why you should start with notebooks and PixieDust, a new open source library that helps data scientists and developers working in the Jupyter Notebook and Apache Spark be more efficient.
Distinguish pop music from heavy metal using Apache Spark MLlib

Distinguish pop music from heavy metal using Apache Spark MLlib

November 25, 2019

Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metaland have fun in the process.
O'Reilly Open Source and Frank Willison Awards

O'Reilly Open Source and Frank Willison Awards

November 22, 2019

The 13th Annual OReilly Open Source Award winners were announced.
Rust for non-Rust developers

Rust for non-Rust developers

November 21, 2019

Hanneli Tavante offers a quick introduction to the Rust language. Drawing on code comparisons between Java/Python and C, Hanneli explores some of Rust's main features, such as its type system, compiler structure, and development environment.
Fear of and uncertainty about open source

Fear of and uncertainty about open source

November 16, 2019

How does a small engineering team decide which technologies to use? Or whether to be open source or not? To be self-hosted or in the cloud? Wes Chow discusses the choices Chartbeat has made, how theyve succeeded and failed, and the framework by which the company makes decisions and argues for transparency and empathy from free and proprietary technologists to ease the pain.
Big data for big data: Machine-learning models of Hadoop cluster behavior

Big data for big data: Machine-learning models of Hadoop cluster behavior

November 9, 2019

Sean Suchter and Shekhar Gupta describe the use of very fine-grained performance data from many Hadoop clusters to build a model predicting excessive swapping events.
PyTorch: A flexible and intuitive framework for deep learning

PyTorch: A flexible and intuitive framework for deep learning

November 3, 2019

James Bradbury offers an overview of PyTorch, a brand-new deep learning framework from developers at Facebook AI Research that's intended to be faster, easier, and more flexible than alternatives like TensorFlow. James makes the case for PyTorch, focusing on the library's advantages for natural language processing and reinforcement learning.
Saving lives with data: Identifying patients at risk of decline

Saving lives with data: Identifying patients at risk of decline

November 2, 2019

Many hospitals combine early warning systems with rapid response teams (RRT) to detect patient decline and respond with elevated care. Predictive models can minimize RRT events by identifying at-risk patients, but modeling is difficult because events are rare and features are varied. Emily Spahn explores the creation of one such patient-risk model and shares lessons learned along the way.
Shifting left for continuous quality in an Agile data world

Shifting left for continuous quality in an Agile data world

November 2, 2019

Data warehouses are critical in driving business decisionswith SQL dominantly used to build ETL pipelines. While the technology has shifted from using RDBMS-centric data warehouses to data pipelines based on Hadoop and MPP databases, engineering and quality processes have not kept pace. Avinash Padmanabhan highlights the changes that Intuit's team made to improve processes and data quality.