Category: Python

Feed: Cloudera Engineering Blog » Cloudera Manager. Author: Justin Kestelyn. The new cluster templates feature in Cloudera Manager 5.7 makes creating clusters faster and easier. Often, after an Apache Hadoop cluster has been configured correctly, its admin will want to replicate the configuration in one or more clusters—whether for promoting a dev or staging cluster to production, or setting up a new production cluster with the same configuration as an existing one. For Cloudera customers, until recently the process for replicating cluster configurations was manual and error-prone. Starting in Cloudera Manager 5.7, the cluster templates feature make this process faster, ... Read More

Feed: Redis Labs. Author: Jay Johnson. Utilizing a machine learning API to create a scalable model artifact pipeline Managing large, pre-trained predictive models across an organization and ensuring the same version is in production can be a challenge with the rapid pace of changes in the AI/machine learning space. Here, we have an approach that demonstrates how to automate building, storing, and deploying predictive models from a Remote Machine Learning Data Store hosted on Redis Labs. This approach is focused on showing how DevOps CI/CD artifact pipelines can be used to build and manage machine learning model artifacts with Jupyter ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Emmanuelle Rieuf. Jupyter notebook content for my OReilly book, the Python Data Science Handbook. This repository contains the full listing of IPython notebooks used to create the book, including all text and code. The code was written and tested with Python 3.5, though most (but not all) snippets will work correctly in Python 2.7. See also the free companion project, A Whirlwind Tour of Python: a fast-paced introduction to the Python language aimed at researchers and scientists. Table of Contents Preface 1. IPython: Beyond Normal Python Help and Documentation in IPython Keyboard Shortcuts ... Read More

Feed: AWS Big Data Blog. Air travel can be stressful due to the many factors that are simply out of airline passengers’ control. As passengers, we want to minimize this stress as much as we can. We can do this by using past data to make predictions about how likely a flight will be delayed based on the time of day or the airline carrier.In this post, we generate a predictive model for flight delays that can be used to help us pick the flight least likely to add to our travel stress. To accomplish this, we will use Apache ... Read More

Feed: Featured Blog Posts - Big Data News. Author: Paul Black. Web scraping (also termed web data extraction, screen scraping, or web harvesting) is a web technique of extracting data from the web, and turning unstructured data on the web  into structured data that can stored to your local computer or a database. The web scraping technique is implemented by web scraping software tools. These tools interacts with websites in the same way as you do when using a web browser like Chrome. In addition to display the data in a browser, web scrapers extract data from web pages and store ... Read More

Feed: Planet Cassandra. Author: Lina Tran. Welcome to the another week of This Week in Cassandra! Cassandra is a distributed database offering linear scalability and high availability. In this post we’ll take a look at projects, blogs, events, jobs and JIRAs that are new to the Cassandra community. We’ll be discussing this post with Jeff Carpenter, Systems Architect at Choice Hotels and author of O’Reilly’s Cassandra: The Definitive Guide, 2nd Edition. News and Blog Posts Cassandra 3.0.8 & Cassandra 2.2.7 & Cassandra 2.1.15 Released! New TimeWindowCompactionStrategy replacing Date Tiered Compaction Sizing Cassandra Data by Adam Hutson of Datascale Cassandra Python ... Read More

Feed: All - O'Reilly Media. Author: Lukas Biewald. Eye of Providence. (source: Bureau of Engraving and Printing on Wikimedia Commons). Object recognition is one of the most exciting areas in machine learning right now. Computers have been able to recognize objects like faces or cats reliably for quite a while, but recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence. Maybe the real surprise is that human brains recognize objects so well. We effortlessly convert photons bouncing off objects at slightly different frequencies into a spectacularly rich set of information about the world around ... Read More

Feed: Planet PostgreSQL. Posted by Devrim GUNDUZ on Wednesday, September 21. 2016 After about 100 hours of packaging and testing work, pgadmin4 RPMs hit PostgreSQL 9.4, 9.5 and 9.6 repos, for RHEL/CentOS 7 and Fedora 23, 24!First of all, I'd like to write down the list of packages that entered git repo for pgadmin4 dependency: python-flask-principal python-flask-babel python-flask python-flask-wtf python-flask-security python-flask-gravatar python-flask-mail python-dateutil django-htmlmin babel python-flask-login python-wsgiref python-fixtures python-pbr python-mimeparse python-flask-sqlalchemy python-pyrsistent python-simplejson python-wtforms python-beautifulsoup4 python-itsdangerous python-sqlalchemy python-werkzeug python-markupsafe pytz python-sqlparse ... Read More

Feed: Microsoft Azure Blog. Author: Kris Zentner. Microsoft Research is at the forefront of solving and tackling cutting edge problems with technologies such as Machine Learning and Deep Neural Networks (DNN). These technologies employ next generation server infrastructure that span immense Windows and Linux cluster environments. Additionally, for DNNs, these application stacks don’t only involve traditional system resources (CPUs, Memory), but also graphic processing units (GPUs). With a nontraditional infrastructure environment, the Microsoft Research Operations team needed a highly flexible, scalable, and Windows and Linux compatible service to troubleshoot and determine root causes across the full stack. Enter Azure Log ... Read More

Feed: Planet Python. Author: . Date Tue, September 20, 2016 I was sitting in my room the other day and thinking about how much we had covered, and I thought I would recap what we’ve learned so far and what lies ahead of us. Up until now we’ve learned: How to break sentences into tokens. The process is called lexical analysis and the part of the interpreter that does it is called a lexical analyzer, lexer, scanner, or tokenizer. We’ve learned how to write our own lexer from the ground up without using regular expressions or any other tools like Lex. How ... Read More