7 years of domain-driven design: Tackling complexity in large-scale marketing systems
March 5, 2020
Vladik Khononov explains how he and his team embraced domain-driven design (DDD) at Plexop, a large-scale marketing system that spans over a dozen different business domains. Join in to learn how DDD allowed the team to manage business complexities, see what worked (and what didn't), and discover where they had to adapt the DDD methodology to fit the company's needs.
Adopting domain-driven design at scale: Near enemies and how to defeat them
March 4, 2020
Everyone doing large-scale software delivery is using domain-driven design (DDD) these days, because it holds the key to delivering maintainable, evolvable solutions with independent teams. But it can go wrong, and then DDD is blamed. Andrew Harmel-Law and Gayathri Thiyagarajan detail a real project they saw fail. You'll learn the many problems they spotted and how they fixed them.
A GDPR retrospective: Implementation by a large-scale data organization in reality
February 29, 2020
GDPR was likely one of the biggest challenges in data management that occurred in 2018. Yulia Trakhtenberg dives into a one-year retrospective about how it was executed in reality at a large-scale data organization.
How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE)
February 19, 2020
Join Thomas Phelan to learn whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.
Large-scale machine learning at Facebook: Implications of platform design on developer productivity
February 19, 2020
AI plays a key role in achieving Facebook's mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Kim Hazelwood and Mohamed Fawzy explain how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.
Architecting a data analytics service both in the public cloud and in the on-premise private cloud: ETL, BI, and machine learning (sponsored by SK Holdings)
February 16, 2020
Jungwook Seo walks you through a data analytics platform in the cloud by the name of AccuInsight+ with eight data analytic services in the CloudZ (one of the biggest cloud service providers in Korea), which SK Holdings announced in January 2019.
Deep learning from scratch
February 15, 2020
You'll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you'll be prepared to dive deeper into the deep learning applications of your choice.
How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE (BlueData))
February 12, 2020
Anant Chintamaneni and Matt Maccaux explore whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.
Scalable anomaly detection with Spark and SOS
February 10, 2020
Jeroen Janssens dives into stochastic outlier section (SOS), an unsupervised algorithm for detecting anomalies in large, high-dimensional data. SOS has been implemented in Python, R, and, most recently, Spark. He illustrates the idea and intuition behind SOS, demonstrates the implementation of SOS on top of Spark, and applies SOS to a real-world use case.
Using Spark for crunching astronomical data on the LSST scale
February 8, 2020
The Large Scale Survey Telescope (LSST) is one of the most important future surveys. Its unique design allows it to cover large regions of the sky and obtain images of the faintest objects. After 10 years of operation, it will produce about 80 PB of data in images and catalog data. Petar Zecevic explains AXS, a system built for fast processing and cross-matching of survey catalog data.
What does the public say? A computational analysis of regulatory comments
February 8, 2020
While regulations affect your life every day, and millions of public comments are submitted to regulatory agencies in response to their proposals, analyzing the comments has traditionally been reserved for legal experts. Vlad Eidelman outlines how natural language processing (NLP) and machine learning can be used to automate the process by analyzing over 10 million publicly released comments.
Container orchestrator to DL workload, Bing's approach: FrameworkLauncher
February 6, 2020
Bing in Microsoft runs large, complex workflows and services, but there was no existing solutions that met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs.
Running large-scale machine learning experiments in the cloud
February 3, 2020
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.
Large-scale automated storage on Kubernetes
January 28, 2020
Managing large stateful applications is tough. Matt Schallert outlines the challenges of automating stateful systems at scale and details how embracing a declarative approach can ease operation and automation of these systems on orchestrators such as Kubernetes. He then demonstrates how to apply this methodology to different types of stateful workloads.
(Self-driving technology and the future autonomous depot-to-depot transport)
January 22, 2020
PlusAI is developing a full stack self-driving technology to enable large-scale autonomous commercial fleets. Hao Zheng examines some of the unique challenges across different layers of the technology stack of building an autonomous truck that's both safe and efficient and dives into how PlusAI is addressing them.
Scaling teams with technology (or is it the other way around?)
January 19, 2020
Microservices and cloud native technologies is the path for building large-scale, distributed systems. Can it do the same for teams? Chen Goldberg leads the Google engineering team building Kubernetes, Istio, GKE, and Anthos and explains how the same tech can help build happy teams.
Security precognition: A look at chaos engineering in security incident response
January 19, 2020
Chaos engineering allows security incident response teams to proactively experiment on recurring incident patterns to derive new information about underlying factors that were previously unknown. Join Aaron Rinehart to explore the hidden costs of security incidents, learn a new technique for uncovering system weaknesses in systems security, and more.
Building resilient serverless systems
January 17, 2020
John Chapin explains howin this brave new world of managed services and platformsyou can use serverless technologies and an infrastructure-as-code mind-set to architect, build, and operate resilient systems that survive even massive vendor outages.