network

Engineer Versus Complexity

Engineer Versus Complexity

December 24, 2019

Network engineers are awash in complexity – but what can we do about it? One approach is to reduce complexity, but often the complexity we face is just a matter o …
NetOps Coding 201 - Building Facebook's FBAR for Network Devices

NetOps Coding 201 - Building Facebook's FBAR for Network Devices

December 24, 2019

Continuing with our theme of automating day-to-day operations, we'll dive into building your very own FBAR! FBAR is a system used by Facebook to handle server and …
The world will see (and just saw) an Internet zombie apocalypse

The world will see (and just saw) an Internet zombie apocalypse

December 24, 2019

Dyn was recently the subject of a major DDoS attack, its first significant disruption in over 15 years of operation. Phil Stanhope shares Dyn's experience before exploring the rapid evolution of multilayer attacks happening on the Internet and outlining the steps to take to deal with them from an ops perspective.
Building an appier web

Building an appier web

December 23, 2019

For too long, the Web has been seen as a poor relation to native apps, but no longer! Weve been able to add websites to our home screens for a while, but with better offline support and notifications and richer metadata, we can finally build web experiences that rival or exceed native apps. Andy Davies explains how these technologies can be combined to deliver great web experiences.
Progressive web apps for a faster experience

Progressive web apps for a faster experience

December 21, 2019

Several APIs are available on modern browsers to help performance, including Service Worker, Push Notifications, NetInfo API, the Cache Storage API, and the older Application Cache. Through hands-on labs, Maximiliano Firtman walks you through using these APIs to create a progressive web app and improve performance and perception on a website.
Distributed consensus: Making impossible possible

Distributed consensus: Making impossible possible

December 18, 2019

Heidi Howard explains how to construct resilient distributed systems on top of unreliable components. Starting almost two decades ago, with Leslie Lamports Paxos protocol, Heidi leads a journey to todays data centers, covering interesting impossibility results and demonstrating how to construct new fault-tolerance systems that you can depend upon everyday.
Delivering near real-time mobility insights at Swisscom

Delivering near real-time mobility insights at Swisscom

December 13, 2019

Swisscom, the leading mobile service provider in Switzerland, also provides data-driven intelligence through the analysis of its mobile network. Its Mobility Insights team works to help administrators understand the flow of people through their location of interest. Franois Garillot explores the platform, tooling, and choices that help achieve this service and some challenges the team has faced.
How a Spark-based feature store can accelerate big data adoption in financial services

How a Spark-based feature store can accelerate big data adoption in financial services

December 12, 2019

Kaushik Deka and Phil Jarymiszyn discuss the benefits of a Spark-based feature store, a library of reusable features that allows data scientists to solve business problems across the enterprise. Kaushik and Phil outline three challenges they facedsemantic data integration within a data lake, high-performance feature engineering, and metadata governanceand explain how they overcame them.
Lessons learned running Hadoop and Spark in Docker

Lessons learned running Hadoop and Spark in Docker

December 11, 2019

Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale environments poses new challenges, especially for big data applications like Hadoop. Thomas Phelan shares lessons learned and some tips and tricks on how to Dockerize your big data applications in a reliable, scalable, and high-performance environment.
Unlocking unstructured text data with summarization

Unlocking unstructured text data with summarization

December 8, 2019

Our ability to extract meaning from unstructured text data has not kept pace with our ability to produce and store it, but recent breakthroughs in recurrent neural networks are allowing us to make exciting progress in computer understanding of language. Building on these new ideas, Michael Williams explores three ways to summarize text and presents prototype products for each approach.
Unlock the power of AI: A fundamentally different approach to building intelligent systems

Unlock the power of AI: A fundamentally different approach to building intelligent systems

December 7, 2019

Mark Hammond explains how Bonsais platform enables every developer to add intelligence to their software or hardware, regardless of AI expertise. Bonsais suite of toolsa new programming language, AI engine, and cloud serviceabstracts away the lowest-level details of programming AI, allowing developers to focus on concepts they want a system to learn and how those concepts can be taught.
Chainer: A flexible and intuitive framework for complex neural networks

Chainer: A flexible and intuitive framework for complex neural networks

December 6, 2019

Open source software frameworks are the key for applying deep learning technologies. Orion Wolfe and Shohei Hido introduce Chainer, a Python-based standalone framework that enables users to intuitively implement many kinds of other models, including recurrent neural networks, with a lot of flexibility and comparable performance to GPUs.
Deep neural network model compression and an efficient inference engine

Deep neural network model compression and an efficient inference engine

December 6, 2019

Neural networks are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. Song Han explains how deep compression addresses this limitation by reducing the storage requirement of neural networks without affecting their accuracy and proposes an energy-efficient inference engine (EIE) that works with this model.
Deeply active learning: Approximating human learning with smaller datasets combined with human assistance

Deeply active learning: Approximating human learning with smaller datasets combined with human assistance

December 6, 2019

Natural-language assistants are the emergent killer app for AI. Getting from here to there with deep learning, however, can require enormous datasets. Christopher Nguyen and Binh Han explain how to shorten the time to effectiveness and the amount of training data that's required to achieve a given level of performance using human-in-the-loop active learning.
End-to-end learning for autonomous driving

End-to-end learning for autonomous driving

December 6, 2019

Urs Muller presents the architecture and training methods used to build an autonomous road-following system. A key aspect of the approach is eliminating the need for hand-programmed rules and procedures such as finding lane markings, guardrails, or other cars, thereby avoiding the creation of a large number of if, then, else statements.
Obstacles to progress in AI

Obstacles to progress in AI

December 5, 2019

The essence of intelligence is the ability to predict. Prediction, perception, planning/reasoning, attention, and memory are the pillars of intelligence. Yann LeCun describes several projects at FAIR and NYU on unsupervised learning, question answering with a new type of memory-augmented network, and various applications for vision and natural language understanding.
The identities of bots: A learning architecture for conversational software

The identities of bots: A learning architecture for conversational software

December 4, 2019

The recent explosion of bots on communication platforms has rekindled the hopes of conversational AI. However, building intelligent and customizable bots is not just bottlenecked by NLP and speech recognition. Our biggest limitation is the inability to modularize the goals of human bot interconnection. Suman Roy explains why we need a layered architecture for bots to learn about us from data.
From silos to a single pane of glass at USA TODAY NETWORK

From silos to a single pane of glass at USA TODAY NETWORK

December 2, 2019

Three years ago, technical teams at USA TODAY NETWORK were completely siloed, making improvements and troubleshooting difficult and often blind to the rest of the technical organization. Bridget Lane and Kris Vincent explain how drastically the teams' tool belts, thought processes, and goals have changed as the company moved from silos to a single pane of glass.
Kubernetes: Crossing the chasm

Kubernetes: Crossing the chasm

December 1, 2019

As Kubernetes enters the mainstream market, we are seeing more use cases that don't fit the original mold, each bringing a new set of challenges. Ian Crosby discusses three specific case studies, the challenges encountered adopting Kubernetes, and the solutions and tooling used to solve them.
Rebuilding the airplane in flight. . .safely

Rebuilding the airplane in flight. . .safely

November 29, 2019

Rewriting the key software component of your platform from scratch is always intimidating. Shannon Weyrick and James Royalty discuss NS1's recent DNS server rewrite and outline the steps the company took to roll it out across its globally distributed network with no downtime.
Smart networking with service meshes

Smart networking with service meshes

November 29, 2019

Over the past year, service meshes have gained significant interest. Most service meshes have two components: a control plane and a data plane. Anubhav Mishra explains what it takes to build a scalable control and data plane. Anubhav also discusses how HashiCorp Consul provides many features like a distributed key-value store and service discovery that make it ideal for a control plane.
Who guards the guardians? Designing for resilience in cluster orchestrators

Who guards the guardians? Designing for resilience in cluster orchestrators

November 28, 2019

Preetha Appan outlines various failure modes ranging from network failures to entire server failures in Nomad, an open source scheduler that supports heterogeneous workloads.
Debugging distributed systems

Debugging distributed systems

November 26, 2019

Distributed systems are hard. They are complicated, hard to understand, and very challenging to manage. But they are critical to modern software, and when they have problems, we need to fix them. Donny Nadolny looks at what it takes to debug a problem in a distributed system like ZooKeeper, walking attendees through the process of finding and fixing one cause of many of these failures.
Jump-starting back-office connections

Jump-starting back-office connections

November 26, 2019

Large-scale cloud networks are constantly driven by the need for improved performance in communication between data centers. Such back-office communication makes up a large fraction of traffic in many cloud environments. Harkeerat Bedi offers an overview of a tool that improves the efficiency of data-center-to-data-center communication by learning the congestion level of links in between.
The fundamentals of building progressive web apps

The fundamentals of building progressive web apps

November 22, 2019

Pete LePage explores the fundamentals of progressive web apps, covering how to architect a single-page web app using the App Shell model, how to identify the different service worker caching strategies and choose the most appropriate one for a use case, and how to implement an installable web app using manifests, metatags, and other techniques.
Deep learning for web-scale text

Deep learning for web-scale text

November 19, 2019

Piotr Mirowski looks under the hood of recurrent neural networks and explains how they can be applied to speech recognition, machine translation, sentence completion, and image captioning.
Protecting individual privacy in a data-driven world

Protecting individual privacy in a data-driven world

November 17, 2019

With the analytic and predictive power of big data comes the responsibility to respect and protect individual privacy. As citizens, we should hold organizations to account; as data practitioners, we must find intelligent ways to analyze data without violating privacy. Jason McFall discusses privacy risks and surveys leading privacy-preserving analysis techniques.
Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra

Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra

November 17, 2019

Which venues have similar visiting patterns? How can we detect when a user is on vacation? Can we predict which venues will be favorited by users by examining their friends' preferences? Natalino Busa explains how these predictive analytics tasks can be accomplished by using Spark SQL, Spark ML, and just a few lines of Scala code.