How to build an evolutionary architecture
March 2, 2020
Around 2017, Antonio Jimenez and Pedro Martos embarked on an ambitious journey: to redefine one of the company's most mission-critical, most complex products from scratch. Join them as they explore how you can achieve an evolutionary architecture from solid foundations such as microservices architecture within a continuous delivery pipeline.
Bridging, not Breaking Tradition: Use Service Mesh Expansion to Connect Legacy Workloads to Kubernetes Services
March 1, 2020
Kubernetes has become a preferred platform for hosting distributed and portable services and applications. With this, Istio service mesh can be deployed to address service discovery, service-to-servic …
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.
M3 and Prometheus: Monitoring at planet scale for everyone
February 27, 2020
Rob Skillington and ukasz Szczsny explore scaling monitoring, alerting, and configurational complexity for a single view of your applications, databases, infrastructure, and operations across all regions using M3 and Prometheus.
What remains of dashboards and metrics without the hype and anti-patterns
February 24, 2020
Open source tools for dashboarding and metrics have seen massive adoption in recent years. Riding the hype, the new, shiny tools are inevitably confronted with overblown expectations and problematic usage patterns, causing frustration and criticism. Bjrn Rabenstein outlines how to use dashboards and metrics effectively rather than condemning them altogether.
Data science and the business of Major League Baseball
February 16, 2020
Using SAS, Python, and AWS SageMaker, Major League Baseball's (MLB's) data science team outlines how it predicts ticket purchasers likelihood to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service.
ThirdEye: LinkedIns business-wide monitoring platform
February 8, 2020
Failures or issues in a product or service can negatively affect the business. Detecting issues in advance and recovering from them is crucial to keeping the business alive. Join Akshay Rai to learn more about LinkedIn's next-generation open source monitoring platform, an integrated solution for real-time alerting and collaborative analysis.
Talking to the machines: Monitoring production machine learning systems
February 3, 2020
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. You'll see new tools for detecting undesirable model behaviors early in large-scale online ML systems.
A decentralized reference architecture for cloud native applications (sponsored by Ballerina)
February 1, 2020
Asanka Abeysinghe explores cell-based architecture, a self-contained composable unit of architecture. The cell is independently scalable. Its independently deployable. Its independently governed. It's part of an ecosystem of cells. A cell-based architecture is a common pattern that any enterprise can connect architecture, implementation, and deployment by making autonomous development teams.
Machine learning vital signs: Metrics and monitoring of AI in production
January 27, 2020
Production artificial intelligence systems are interacting with the real world, and it's terrifying that oftentimes nobody has any idea how they're performing on live data. Donald Miner details why you should track your models in production over time, explains how you can implement proper logging and metrics for models, and details metrics you should probably be capturing.
Observability for data pipelines: Monitoring, alerting, and tracing lineage
January 27, 2020
Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain and iterate even after they are initially built and validated. Jiaqi Liu explores how to factor in monitoring, alerting, and tracing data lineage when building data applications that move and transform data across multiple dependencies.
Removing unfair bias in machine learning using open source (sponsored by IBM)
January 25, 2020
ML models are increasingly used to make decisions that impact lives. Ana Echeverri and Trisha Mahoney walk you through how to use the open source Python package AI Fairness 360, developed by IBM researchers, a comprehensive open source toolkit empowering users with metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.