A fairy tale about habits; Or what we can learn from Cinderella and her peers in DevOps
February 29, 2020
Like Cinderella's "The good in the potty, the bad in the croppy," Sabine Wojcieszak explains why you should take a closer look at your habits and decide which of them will support your DevOps endeavors and which will harm them.
Accelerating engineering delivery tempo
February 29, 2020
The Splice engineering team grew almost 10 times in 18 months. The delivery practices that worked when it was 5 people broke way before it got to 50. Juan Pablo Buritica explains how the engineering team accelerated delivery using industry insights and data.
Controlled chaos: The inevitable marriage of DevOps and security
February 28, 2020
Software is eating the world, and security will be eaten as well if it doesn't evolve. Kelly Shortridge exposes why chaos and resilience engineering represents the future of security programsand why it catalyzes the dawn of defensive innovation. You'll examine how adopting distributed, immutable, and ephemeral infrastructure (the "DIE" triad) can create powerful security benefits.
Helping your dev teams succeed at ops, post-Kubernetes
February 27, 2020
Michael Hobbs takes a look at how best to ensure your service owners can succeed with responsibilities and concerns that were traditionally the domain of ops teams prior to the deployment of Kubernetes for production load within a business.
The elephant in the Kubernetes room: Team interactions
February 25, 2020
Regardless of all the technical benefits that Kubernetes brings, team interactions are still key for successfully delivering and running services. Manuel Pais explores how team design affects the success of Kubernetes adoption.
From whiteboard to production: A demand forecasting system for an online grocery shop
February 13, 2020
Data-driven software is revolutionizing the world and enable intelligent services we interact with daily. Robert Pesch and Robin Senge outline the development process, statistical modeling, data-driven decision making, and components needed for productionizing a fully automated and highly scalable demand forecasting system for an online grocery shop for a billion-dollar retail group in Europe.
Introducing Kubeflow (with special guests TensorFlow and Apache Spark)
February 4, 2020
Modeling is easyproductizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karaua system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes.
Integrating security into modern software development: A workflow study
January 28, 2020
Application security testing has been around for a long time, yet successful attacks continue despite significant investments in application security. Shift left isnt enough for modern software development that needs integrated and automated continuous security testing. Lucas Charles looks at three key considerations to get you there.
Solving enterprise DevOps and frontend challenges with open source (sponsored by HPE)
January 24, 2020
Almost everyone's looking to streamline the way they develop apps and deploy them. Taking advantage of an easy-to-use open source UI component library (such as grommet.io) to create responsive, mobile-first projects is the way to go. Join Pramod Sareddy to learn how Open Service Broker saves you time.
Unlocking your serverless functions with OpenFaaS for AI chatbot projects
January 23, 2020
Sergio Mendez examines critical challenges when implementing AI chatbots and explains how Movistar designed an open source serverless architecture using OpenFaaS on top of Kubernetes and other complementary technologies like NoSQL, brokers to deploy Telegram AI chatbots. Sergio then compares these technologies to "vendor lock-in" services offered by major cloud providers.
What's your machine learning score?
January 23, 2020
ML in production is different than ML in an R&D environment. Tania Allard dives deep into a number of techniques to test your ML quality and decay in your R&D and production environments appropriately. You'll see examples of issues commonly encountered in the ML area and how to test and monitor your data, model development, and infrastructure.
Kick-starting a culture of observability and data-driven DevOps (sponsored by SignalFx)
January 20, 2020
Rajesh Raman dives deep into the practice of observability, demonstrating how a more analytics-driven approach to metrics, traces, and other monitoring signals improves observability. You'll learn a framework for kick-starting a culture of observability in your organization, informed by Rajesh's experience building and deploying observability tools at SignalFx.
Product management and DevOps, together at last and kicking butt
January 19, 2020
DevOps and platform teams have too many projects, not enough time, and users who can easily ask if the thing is done, because "it's really holding them up." James Heimbuck explores the good, the bad, and the ugly of how SendGrid incorporates product management practices into planning and execution within DevOps and platform teams to cut off scope creep and never-ending projects and realize value.
The SRE I aspire to be
January 18, 2020
Yaniv Aknin dives into the secret sauce for a successful SRE organization: high-quality measurements of reliability. He explains why measuring reliability is crucial (and why its so hard), shares a couple of tips for getting it right, and explores why its the key differentiator between SRE and DevOps.
Hands-on introduction to Kubernetes and OpenShift
January 16, 2020
Join Christian Hernandez to learn Kubernetes basics using curl, kubectl, oc, and other command-line tools. You'll discover how to model portable, scaleable, and highly available solutions using open source tools for distributed computing.
Data-driven digital transformation and jobs: The new software hierarchy and ML
January 13, 2020
Robert Cohen discusses the skills that employers are seeking from employees in digital jobs, linked to the new software hierarchy driving digital transformation. Robert describes this software hierarchy as one that ranges from DevOps, CI/CD, and microservices to Kubernetes and Istio. This hierarchy is used to define the jobs that are central to data-driven digital transformation.
Bringing your machine learning to production with ML Ops (sponsored by Microsoft)
January 2, 2020
Sarah Bird offers an overview of ML Ops (DevOps for machine learning), sharing solutions and best practices for an end-to-end pipeline for data preparation, model training, and model deployment while maintaining a comprehensive audit trail. Join in to learn how to build a cohesive and friction-free ecosystem for data scientists and app developers to collaborate together and maximize impact.