4Developers 2019
2019-04-08
Warsaw, Poland
[4Developers 2015, 4Developers 2017, 4Developers 2019, 4Developers 2019 Warszawa, 4Developers 2020, 4Developers 2021, 4Developers 2022 Online]
| title | author | length |
|---|---|---|
| Artificial Intelligence in the Cloud (...) | Damian Mazurek | 41m |
Presentation explains combining cloud platforms and AI by showing a case study of an industrial image-classification system that met client requirements using… | ||
| The Client Had a Dream, or From Zero to a Cloud Solution | Tomasz Marciniak | 46m |
Covers turning a vague client vision with fixed deadline and budget into a cloud solution by scoping an achievable MVP and avoiding overbuilding. Discusses… | ||
| Zero-downtime Deployment Strategies (...) [ENG] | Wojciech Barczyński | 37m |
Live demos show concrete zero‑downtime patterns implemented on Kubernetes with mentions of Traefik and Istio, including how to write readiness and liveness… | ||
| Serverless approach in Azure - another buzzword or 'Holy Grail'(...)? | Paweł Fiderek | 45m |
Examines Microsoft Azure serverless options (BaaS and FaaS) and evaluates whether they deliver on cloud providers’ promises for reliability, performance, and… | ||
| The Most Popular Architectural Patterns in the Cloud | Łukasz Kałużny | 45m |
Covers key cloud architecture principles and patterns for building highly available, reliable, performant and secure applications. Explains specific… | ||
| Serverless in the Cloud (...) | Michał Furmankiewicz, Przemysław Malak | 47m |
An advanced talk comparing serverless functions across Azure and AWS, showing four short demos to highlight differences, strengths, and weaknesses of each… | ||
| Azure SQL - more or/and less than SQL Server | Rafał Hryniewski | 47m |
Microsoft Azure SQL Database is presented as a relational PaaS offering that is often mistaken for a one-to-one cloud copy of SQL Server. The talk explains… | ||
| How to Build a Solution Processing Terabytes of Data (...)? | Kamil Dworak | 36m |
A practical case of processing terabytes of time-series meter data from 500,000 devices (11 channels, 10–15 minute intervals) and scaling the same pipeline to… | ||
| nothing matches | ||