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.
The dark side of events
March 1, 2020
Events are our industrys near and dear. All technological conferences are full of talks on event sourcing, event-driven architectures, or event-driven integrations. Vladik Khononov adds another one, but a bit different. Lets talk about the dark side of this patternthe cases in which events turn into an anti-pattern, how to identify them, and, of course, how to turn the project around.
Building a data ecosystem at Sweden's Television: Lessons and pitfalls
February 29, 2020
Sweden's Television manages online products that range from providing news to TV series and are used by millions of people. To make sure that it creates content that engages, entertains, and educates, it started its own platform for collecting and analyzing user data. Ismail Elouafiq highlights the architectural choices the company made and the lessons it learned in building its data ecosystem.
Prioritizing trust while creating applications
February 26, 2020
Time and money are generally the resources we focus on when building applications. Yet we cant buy trust; it builds slowly and can be broken quickly when we dont factor it in to our development process. Jennifer Davis examines how to leverage security practices to enable an all-team approach to security.
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.
The ultimate guide to complicated systems
February 25, 2020
Building and maintaining distributed systems is hard. Industry tools and recommended practices are evolving at an ever-increasing velocity. New platform choices reduce infrastructure management and add operational complexity obscuring the value of operation skills. Often, bureaucratic decisions drive practices and tool choices.
MLIR: Accelerating AI
February 23, 2020
MLIR is TensorFlow's open source machine learning compiler infrastructure that addresses the complexity caused by growing software and hardware fragmentation and makes it easier to build AI applications. Chris Lattner and Tatiana Shpeisman explain how MLIR is solving this growing hardware and software divide and how it impacts you in the future.
Deep learning with Horovod and Spark using GPUs and Docker containers
February 20, 2020
Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment.
Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data
February 19, 2020
Voiced-based AI continues to gain popularity among customers, businesses, and brands, but its important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Andreas Kaltenbrunner examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.
Executive Briefing: The black boxInterpretability, reproducibility, and data management
February 19, 2020
The growing complexity of data science leads to black box solutions that few people in an organization understand. Mark Madsen explains why reproducibilitythe ability to get the same results given the same informationis a key element to build trust and grow data science use. And one of the foundational elements of reproducibility (and successful ML projects) is data management.
Building an AI platform: Key principles and lessons learned
February 16, 2020
Moty Fania details Intels IT experience of implementing a sales AI platform. This platform is based on streaming, microservices architecture with a message bus backbone. It was designed for real-time data extraction and reasoning and handles the processing of millions of website pages and is capable of sifting through millions of tweets per day.
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.
Orchestrating data workflows using a fully serverless architecture
February 11, 2020
Use of data workflows is a fundamental functionality of any data engineering team. Nonetheless, designing an easy-to-use, scalable, and flexible data workflow platform is a complex undertaking. Tomer Levi walks you through how the data engineering team at Fundbox uses AWS serverless technologies to address this problem and how it enables data scientists, BI devs, and engineers move faster.
Deep learning at scale: Tools and solutions
February 6, 2020
Success with DL requires more than just TensorFlow or PyTorch. Angela Wu, Sidney Wijngaarde, Shiyuan Zhu, and Vishnu Mohan detail practical problems faced by practitioners and the software tools and techniques you'll need to address the problems, including data prep, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, mobile and edge optimization, and more.
Delivering AI vision ecosystem offers with Intel AI: In Production
February 6, 2020
Join Lindsay Hiebert and Vikrant Viniak as they explore challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem to accelerate prototype to production at the edge with Intel and partner offerings.
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.