Computing at scale

This document discusses preparing for content delivery network (CDN) failures and how to monitor CDN performance. It provides examples of past CDN outages and failures. It then covers different methods for monitoring CDN performance, including synthetic monitoring and real user monitoring. It emphasizes the importance of measuring failure rates not just speeds. The document also discusses mitigating CDN failures through a multi-CDN approach with dynamic traffic steering based on performance data. It notes some challenges in decision making and with low volume data. Finally, it shares a story about responding to an outage at a company.

Kaggle competitions are great, but what do you do when you have a cool idea for your own machine-learning project? Learn about all the dirty data, bugs of others, and keeping it all running, when building from zero to production. Hear about the mistakes that I've made so you can avoid them yourself.

The story of how solving one problem the OpenSource way opened doors to so much more. Talk presented by Pranav Prakash and Hari Prasanna at OSDConf 2014, New Delhi.

This document outlines the steps in the data science process from raw data to deployment using KNIME. It begins with an overview of the CRISP-DM process and then discusses each step in more detail, including data preparation, model training, optimization, evaluation, and deployment. It then provides an example classification problem using airline departure delay data and outlines challenges for participants to work through in data access/preparation, model training/optimization, and deployment. Contact information is also provided for KNIME resources.

This document summarizes a presentation about using Python and Apache TinkerPop to work with graph databases. It discusses Gremlin, a graph traversal language, and how Gremlin has been incorporated into Python through Gremlin-Python. It provides an example of building a small web application and APIs to work with an air routes graph stored in a graph database, and deploying the application and database to the cloud.

A Python application that uses artificial intelligence to predict stock prices. Student project in the AIClub Python with AI Class

Drupal DOMinate was presented by Matt Wrather and Steven Rifkin at the Los Angeles Drupal User Group meetup 5/14/13. The presentation covers the use of the Drupal Javascript API focusing on the behaviors and settings objects.

Slides from my Velocity NY 2014 talk, "Infrastructure as Code in Government" http://velocityconf.com/velocityny2014/public/schedule/detail/35839

The document discusses using Abraham Maslow's hierarchy of needs as a framework for developing contextual applications with the Device API. It outlines each level of the hierarchy - physiological, safety, love/belonging, esteem, and self-actualization - and provides examples of how existing and future Device API features could address needs within each level, from low-level concerns like connectivity and battery life to higher-level goals around relationships and personal growth. The overall message is that the expanding capabilities of the Device API open opportunities to create applications that respond to users' context in holistic ways.

An overview of the different Cloud technologies available from Google including App Engine, Google Storage, Google Prediction API, and BigQuery. This presentation was given to the San Diego GTUG on Aug 26th, 2011.

An overview of the different Google Cloud Technologies. Includes coverage of Google App Engine, Google Storage, Google Prediction Api, and BigQuery. This presentation was given to the San Diego GTUG on Aug 26th, 2011.

This is the presentation "Building Integrated Applications on Google's Cloud Technologies" that was given at GDD 2011 #gdd11 in Sao Paulo and Buenos Aires by Google Developer Advocate Chris Schalk @cschalk.

- The document discusses Google's Prediction API which allows users to build machine learning models and make predictions by uploading training data, training models on that data, and then making predictions on new data. - It provides an example of using the Prediction API to automatically categorize and respond to customer emails by language by training on tagged emails and predicting the language of new emails. - The process involves uploading training data, training a model on that data, and then making predictions on new data using the trained model to receive a predicted language label.

This is a presentation on how to use the different Google Cloud technologies to build applications. It was delivered in Mexico City at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th, 2011 in Mexico City by Google Developer Advocate Chris Schalk.

This document provides an introduction to Google's cloud technologies including Google App Engine, Google Storage, the Prediction API, and BigQuery. It describes each technology's capabilities and how developers can use them. Google App Engine is an application development platform, Storage provides cloud data storage, Prediction API enables machine learning predictions, and BigQuery allows fast, SQL-based analysis of large datasets. Examples and demos of each technology are also presented.

This document introduces several new Google cloud technologies: Google Storage for storing data in Google's cloud, the Prediction API for machine learning and predictive analytics, and BigQuery for interactive analysis of large datasets. It provides overviews and examples of using each service, highlighting their capabilities for scalable data storage, predictive modeling, and fast querying of massive amounts of data.

Presentation given by Google Developer Advocate Chris Schalk on building integrated applications with Google's Cloud Technologies.

This is a presentation given by Google Developer Advocate Chris Schalk at Spring One 2GX on Oct 21st, 2010. It introduces Google Storage for Developers, Prediction API, and BigQuery.