Tag Google

High Volume Distributed Tracing with Ben Sigelman

http://traffic.libsyn.com/sedaily/DistributedTracing.mp3Podcast: Play in new window | Download You are requesting a car from a ridesharing service such as Lyft. Your request hits the Lyft servers and begins trying to get you a car. It takes your geolocation, and passes the geolocation to a service that finds cars that are nearby, and puts all those cars into a list. The list of nearby cars is sent to another service, which sorts

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Istio Motivations with Louis Ryan

http://traffic.libsyn.com/sedaily/IstioMotivations.mp3Podcast: Play in new window | Download A single user request hits Google’s servers. A user is looking for search results. In order to deliver those search results, that request will have to hit several different internal services on the way to getting a response. These different services work together to satisfy the user request. All of these services need to communicate efficiently, they need to scale, and they need

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Machine Learning and Technical Debt with D. Sculley Holiday Repeat

http://traffic.libsyn.com/sedaily/ml_techdebt_ad_free.mp3Podcast: Play in new window | DownloadOriginally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High Interest Credit Card of Technical Debt. Questions How do

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Protocol Buffers with Kenton Varda

http://traffic.libsyn.com/sedaily/ProtocolBuffers.mp3Podcast: Play in new window | Download When engineers are writing code, they are manipulating objects. You might have a user object represented on your computer, and that user object has several different fields—a name, a gender, and an age. When you want to send that object across the network to a different computer, the object needs to be turned into a sequence of 1s and 0s that will travel

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High Volume Logging with Steve Newman

http://traffic.libsyn.com/sedaily/Scalyr.mp3Podcast: Play in new window | Download Google Docs is used by millions of people to collaborate on documents together. With today’s technology, you could spend a weekend coding and build a basic version of a collaborative text editor. But in 2004 it was not so easy. In 2004 Steve Newman built a product called Writely, which allowed users to collaborate on documents together. Initially, Writely was hosted on a

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BigQuery with Jordan Tigani

http://traffic.libsyn.com/sedaily/BigQuery.mp3Podcast: Play in new window | Download Large-scale data analysis was pioneered by Google, with the MapReduce paper. Since then, Google’s approach to analytics has evolved rapidly, marked by papers such as Dataflow and Dremel. Dremel combined a column-oriented, distributed file system with a novel way of processing queries. A single Dremel query is distributed into a tree of servers, starting with the root server, splitting into the intermediate servers,

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Dremio with Tomer Shiran

http://traffic.libsyn.com/sedaily/Dremio.mp3Podcast: Play in new window | Download The MapReduce paper was published by Google in 2004. MapReduce is an algorithm that describes how to do large-scale data processing on large clusters of commodity hardware. The MapReduce paper marked the beginning of the “big data” movement. The Hadoop project is an open source implementation of the MapReduce paper. Doug Cutting and Mike Cafarella wrote software that allowed anybody to use MapReduce,

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Model Training with Yufeng Guo

http://traffic.libsyn.com/sedaily/ModelTraining.mp3Podcast: Play in new window | Download Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function and each of those data points. Once I

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Istio Service Mesh with Varun Talwar and Louis Ryan

http://traffic.libsyn.com/sedaily/IstioServiceMesh.mp3Podcast: Play in new window | Download Modern software applications are often built out of loosely coupled microservices. These services can be written in different languages, by different people, but communication between services needs to be standardized. For this reason, a service proxy is useful. A service proxy is a sidecar container that sits next to a service and facilitates communications with other services. Once every service has a sidecar

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Search Engine Land with Danny Sullivan

http://traffic.libsyn.com/sedaily/SearchEngineLand.mp3Podcast: Play in new window | Download Search engines run our lives. The path we take to information is dictated by Google, Facebook, Amazon, and other forms of search. Search engines feel objective and truthful, but are built through ongoing experimentation and subjective decision making. That’s what has kept Danny Sullivan writing about search engines for twenty years. The content Google prioritizes, the ads that we see, the way that

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Google Early Days with John Looney

http://traffic.libsyn.com/sedaily/googleearlydays_edited.mp3Podcast: Play in new window | Download John Looney spent more than 10 years at Google. He started with infrastructure, and was part of the team that migrated Google File System to Colossus, the successor to GFS. Imagine migrating every piece of data on Google from one distributed file system to another. In this episode, John sheds light on the engineering culture that has made Google so successful. He has

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Container Engines with David Aronchick and Chen Goldberg

http://traffic.libsyn.com/sedaily/containerengines.mp3Podcast: Play in new window | Download Kubernetes makes it easier for engineering teams to manage their distributed systems architecture. But it’s still not simple to deploy and operate a Kubernetes cluster. Google Container Engine (GKE) is a managed control plane for Kubernetes. Just as developers can use Google App Engine to easily deploy monolithic apps against a platform as a service, we can use Google Container Engine to deploy

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Firebase with Doug Stevenson

http://traffic.libsyn.com/sedaily/Firebase.mp3Podcast: Play in new window | Download Firebase is a backend-as-a-service. The key efficiency of a backend-as-a-service is that it enables developers to go from having a 3-tier architecture (client, server, database) to a 2-tier architecture (client, backend-as-a-service). The team who started Firebase built it as a pivot. They had started a social network, and then they realized there wasn’t a good backend for chat tools. And so they started

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Google Brain Music Generation with Doug Eck

http://traffic.libsyn.com/sedaily/GoogleBrain.mp3Podcast: Play in new window | Download Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music today consists of simple melodies without much chord

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Cloud Clients with Jon Skeet

http://traffic.libsyn.com/sedaily/CloudClients_edited.mp3Podcast: Play in new window | Download Google builds cloud services for developers, such as PubSub, Cloud Storage, BigQuery, and Cloud DataStore. On Software Engineering Daily, we’ve done lots of shows about how these types of services are built. In this episode, we are zooming in on the interaction between the developer using a cloud service and the design and engineering of the client APIs. To build a useful cloud

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TensorFlow in Practice with Rajat Monga

http://traffic.libsyn.com/sedaily/TensorFlow_with_Rajat__Edited.mp3Podcast: Play in new window | Download TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the process of deploying a machine learning model from

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Kubernetes Origins with Craig Mcluckie

http://traffic.libsyn.com/sedaily/Kubernetes_Origins_Edited.mp3Podcast: Play in new window | Download The container management system Kubernetes was open sourced by Google with the intention of creating a cloud service based on the project. Today, the Kubernetes ecosystem is looking similar to the Android ecosystem, with different vendors providing different ways to use Kubernetes, from RedHat’s OpenShift to Google Container Engine.   Craig Mcluckie was a member of the team who originally devised Kubernetes, and

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Management and Hiring with Jon Emerson

http://traffic.libsyn.com/sedaily/Management.mp3Podcast: Play in new window | Download Engineering managers start out as engineers. Eventually, there is a fork in their career road where an engineer can choose to move up into management or continue on as an engineer in a more senior role. Changing to management involves an increase in responsibilities, a different set of goals to focus on. Jon Emerson was working at Google as an engineer when a

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Google’s Polymer Project with Rob Dodson

http://traffic.libsyn.com/sedaily/Polymer_Edited.mp3Podcast: Play in new window | Download Smart phone apps have better performance than web apps. When we have an application that we use on a regular basis, we download that application to a smart phone rather than using the browser based version on our mobile browser. Google’s Polymer Project wants to improve the gap between native app performance and mobile web app performance. The key problem with mobile web

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Deep Learning and Keras with François Chollet

“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.”

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