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XConf North America 2021

software architects, platform engineers, data engineers, machine learning engineers, devops engineers, engineering managers, product managers

evolutionary architecture and fitness functions; continuous delivery and CI safety nets; consumer-driven contracts for services; Azure Kubernetes Service deployments; Grafana dashboards and observability stacks; CD4ML model pipelines and versioning; data mesh testing and data product packaging; federated computational governance; architecture decision records (ADRs); explainable AI methods and counterfactuals; developer portals and self-serve platforms; value stream optimization and DORA signals

titleauthorlength
The past, present and future of ArchitectureRebecca Parsons58m
Argues that architecture must be evolutionary — explicitly guided and measurable across reliability, scalability, security and maintainability — enabled by…
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Observability in digital transformationAjay Chankramath, Sean Alvarez46m
A mid‑market finance rollout builds a low-cost observable aggregator that preserves source systems while offering a single pane of glass, ingesting event…
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Getting Machine Learning applications into production using MLOps and CD4MLEric Nagler14m
87% of data science projects never reach production, so CD4ML lays out a concrete six-step pipeline — model building, evaluation, productionization, testing…
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Product innovation, not derivationDave Ho19m
Argues that limited technical acumen forces product teams into copycat features and lower success rates—about 70% failure for new features/products—and gives…
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Lightweight architecture governance in the largeImran Khan, Leo Mattos16m
Describes a lightweight governance framework proven on a multi‑year enterprise modernization that moves teams from 5 to 15+ and across acquisitions, showing…
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Using Explainable AI to garner trust and overcome adoption inhibitorsAshley Avery26m
95% cite cultural, organizational and process barriers to AI adoption, and trust—not just transparency—is the bottleneck. Presents concrete XAI methods: shop…
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Augmented Reality BasicsParis Kaman10m
Augmented reality depends on computer vision not hardware hype and uses three concrete anchoring methods: image targets, face meshes, and anchors from feature…
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Data Mesh testing: An opinionated view of what good looks likeSofia Tania20m
A practical 15-minute roadmap that prescribes specific tests per product type: unit and component tests for transformation logic, data quality checks and…
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Optimizing the engineering value streamPrem C15m
Argues that elite teams converge on three traits: rapid, reliable, repeatable delivery and that the State of DevOps four metrics — deployment frequency, lead…
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Data Mesh: Lessons from the trenchesSina Jahan, Storm Heidinger44m
Large Fortune‑100 healthcare payer tries to move analytics to the cloud with a goal of ~1,000 assets migrated, but finds success only by building a custom…
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The uncanny valley of hybrid workingTom Marsh14m
Argues that team-level dynamics determine whether co-location, remote, or hybrid works and that hybrid often loses most benefits: ideal co-location scales to…
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I don't think these metrics mean what you think they mean!Ranbir Chawla14m
Argues that DORA metrics are lagging indicators and instead recommends leading signals: measure cycle time and inner cycles, limit WIP, track error budgets and…
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Developer portalTim Cochran, Ama Asare15m
Joiners often waste hours hunting docs across Confluence, Slack and drives; a developer portal claims to stop that by centralizing a Backstage service catalog…
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Scaling a crypto currency exchange startupRickey Zachary47m
Explains that crypto exchanges must handle 3,000% traffic spikes and roughly 300× user growth from single social posts, so availability, consistency and…
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Thoughtworks culture: 10,000 and beyondVenki Ponniah, Patricia Mandarino36m
Thoughtworks claims a 28‑year culture that produced concrete tools and practices: Selenium and CruiseControl and a microservices paper that become industry…
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