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Building resilient serverless systems

Building resilient serverless systems

February 12, 2020

John Chapin explains howin this brave new world of managed services and platformsyou can use serverless technologies and an infrastructure-as-code mindset to architect, build, and operate resilient systems that survive even massive vendor outages.
Comparing Next-Generation Container Image Building Tools

Comparing Next-Generation Container Image Building Tools

February 11, 2020

Until recently, running docker build against Dockerfile had been the only way to build container images. However, lots of opensource software are being proposed as successors/alternatives to `docker …
FaaS Shell: Multi-cloud Portable Serverless Function Workflow

FaaS Shell: Multi-cloud Portable Serverless Function Workflow

February 10, 2020

Serverless Function Workflow is available from each FaaS for their own such as AWS Step Functions, Azure Logic Apps, IBM Function Composer, Fission Flow, and etc. Hence workflow isn't portable. This …
SNMP is dead

SNMP is dead

February 9, 2020

Modern networks have significantly outpaced the monitoring capabilities of SNMP and command-line scraping. Over the last three years we at Google have been workin …
Tutorial: Constructing App Streaming Cloud Infrastructure Using Android Containers - Dr.

Tutorial: Constructing App Streaming Cloud Infrastructure Using Android Containers - Dr.

February 5, 2020

In January, 2018, ICL/ITRI CBA (Container-based Android) team released the Android containerization implementation as Android Containerization open source project on github. The team would like to s …
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

February 4, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI

Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI

February 4, 2020

We're experiencing an AI gold rush. Tech giants, corporations, startups, and governments are investing billions, and headlines about AI have reached fever pitch. It's dizzying to keep track of the latest AI developments and claims. Join Simon Greenman to learn who can and who will make money in this gold rushand who will become economic casualties along the way.
Machine learning at scale with Kubernetes

Machine learning at scale with Kubernetes

February 3, 2020

Christopher Cho details how to leverage Kubernetes and the mighty Kubernetes APIs to build a complete deep learning pipeline, from data ingestion and aggregation to preprocessing and ML training to serving. Along the way, Christopher covers Kubeflow, a Google open source solution for managing machine learning with TensorFlow in a portable, scalable manner.
The missing piece

The missing piece

February 2, 2020

Why do businesses fail at machine learning despite its tremendous potential and the excitement it generates? Is the answer always in data, algorithms, and infrastructure, or is there a subtler problem? Will things improve in the near future? Cassie Kozyrkov shares lessons learned at Google and explains what they mean for applied data science.
Building successful site reliability engineering in large enterprises

Building successful site reliability engineering in large enterprises

February 1, 2020

Implementing site reliability (SRE) engineering doesn't have to be intimidating, and it isn't only for cloud-native organizations. Liz Fong-Jones and Dave Rensin share eight key lessons Google's customer reliability engineering team learned helping large enterprises adopt SRE as an operations engineering model.
Bulk image processing using Kubernetes

Bulk image processing using Kubernetes

February 1, 2020

Mike Newswanger explains how he used Kubernetes and Google Cloud to burst and extend the capacity of a physical infrastructure for optimizing almost 10 million images in less than two weeks.
Knative: Kubernetes, serverless, and you

Knative: Kubernetes, serverless, and you

January 30, 2020

It's a Kubernetes world. Join Ryan Gregg to learn about Knative, an open source collaboration between Google and other industry leaders to define the future of serverless on Kubernetes. Knative solves the difficult but boring aspects of running modern cloud applications on Kubernetes.
Migrating Spotify's runtime to Kubernetes

Migrating Spotify's runtime to Kubernetes

January 30, 2020

Spotify recently completed the migration of all services from running on bare-metal hardware to hosts in the cloud on GCP. Spotify is now in the exciting process of journeying from merely cloud hosted to cloud native via migrating the running of services to Kubernetes. James Wen discusses the work involved, lessons learned, and pitfalls encountered in moving services onto Kubernetes.
SLO burn

SLO burn

January 29, 2020

Jamie Wilkinson offers a brief overview of SLOs, shares a practical guide to implementing sustainable SLO-based alerting for systems of any size, and outlines the tooling required to supplement the system in the absence of cause-based alerting.
Tracing polyglot systems: An OpenTracing tutorial

Tracing polyglot systems: An OpenTracing tutorial

January 28, 2020

Priyanka Sharma and Yuri Shkuro demonstrate how distributed tracing works and how to employ it in the development and operations of your applications in the programming language of your choice: Java, Go, Python, Node.js, C#, or C++.
Applying petabyte-scale analytics and machine learning to billions of news reading sessions

Applying petabyte-scale analytics and machine learning to billions of news reading sessions

January 26, 2020

What can we learn from a one-billion-person live poll of the internet? Andrew Montalenti explains how Parse.ly has gathered a unique dataset of news reading sessions of billions of devices, peaking at over two million sessions per minute on thousands of high-traffic news and information websites, and how the company uses this data to unearth the secrets behind online content.
Best practices for developing an enterprise data hub to collect and analyze 1 TB of data a day from a multiple services with Apache Kafka and Google Cloud Platform

Best practices for developing an enterprise data hub to collect and analyze 1 TB of data a day from a multiple services with Apache Kafka and Google Cloud Platform

January 26, 2020

Recruit Group and NTT DATA Corporation have developed a platform based on a data hub, utilizing Apache Kafka. This platform can handle around 1 TB/day of application logs generated by a number of services in Recruit Group. Kenji Hayashida and Toru Sasaki share best practices for and lessons learned about topics such as schema evolution and network architecture.
Cassandra versus cloud databases

Cassandra versus cloud databases

January 25, 2020

Is open source Apache Cassandra still relevant in an era of hosted cloud databases? Jonathan Ellis discusses Cassandras strengths and weaknesses relative to Amazon DynamoDB, Microsoft CosmosDB, and Google Cloud Spanner.
Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

January 25, 2020

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.
Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

Conda, Docker, and Kubernetes: The cloud-native future of data science (sponsored by Anaconda)

January 25, 2020

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

January 24, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Managing data science in the enterprise

Managing data science in the enterprise

January 20, 2020

The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders deliver measurable impact on an increasing share of an enterprises KPIs. Joshua Poduska and Patrick Harrison detail how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage
The Missing Piece

The Missing Piece

January 17, 2020

Why do businesses fail at machine learning despite its tremendous potential and the excitement it generates? Is the answer always in data, algorithms, and infrastructure, or is there a subtler problem? Will things improve in the near future? Let's talk about some lessons learned at Google and what they mean for applied data science.
AI on Kubernetes

AI on Kubernetes

January 14, 2020

Kubernetesthe container orchestration engine used by all of the top technology companieswas built from the ground up to run and manage highly distributed workloads on huge clusters. Thus, it provides a solid foundation for model development. Daniel Whitenack demonstrates how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes.
Customized ML for the enterprise (sponsored by Google Cloud)

Customized ML for the enterprise (sponsored by Google Cloud)

January 14, 2020

Levent Besik explains how can enterprises stay ahead of the game with customized ML in our ever-changing world of AI capabilities and limited data science resources.
Evaluate deep Q-learning for sequential targeted marketing with 10-fold cross-validation

Evaluate deep Q-learning for sequential targeted marketing with 10-fold cross-validation

January 13, 2020

Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards.
Executive Briefing: Best practices for human in the loopThe business case for active learning

Executive Briefing: Best practices for human in the loopThe business case for active learning

January 12, 2020

Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google)

Frontiers of TensorFlow: Space, statistics, and probabilistic ML (sponsored by Google)

January 12, 2020

Join in for two talks on TensorFlow in space and mathematics. Josh Dillon discusses TensorFlow Probablity (TFP), and Wahid Bhimji discusses deep learning for fundamental sciences using high-performance computing.