Sonic Project EU·Oct 26Cybersecurity is a Team Effort: Why European Collaboration MattersEuropean collaboration creates opportunities to combine expertise, share knowledge and develop cybersecurity technologies through joint…
Roysegal·Oct 19Forecasting Real Fluid Flows: Second Place in Both RealPDE TracksOur approach to representation, augmentation, uncertainty, and learning from a recording’s history.
APARNA KOTAKONDA·Jul 20GCP-PDE — Giving Every Workload the Right Lane — Organising Workloads (Section 5.3)A shared road where a bicycle and a truck compete for the same space isn’t a road system. It’s a bottleneck waiting to happen.
APARNA KOTAKONDA·Jul 20GCP-PDE — The Pipeline That Runs Itself — Designing Automation and Repeatability (Section 5.2)A pipeline that needs a human to press start every morning isn’t automated. It’s just convenient manual work.
InDancing Elephants PressbyAnnelise Lords·Jun 28DEP Chronicles —#26-Career, Dreams and AmbitionsMy dreams and career didn’t go as I wanted them to. But my ambitions allow it to go how it should.A response icon31A response icon31
InWanaData AfricabyWanaData·May 18Bourse EPD-DDI WanaData Cohorte II: Pour une meilleure visibilité des données de genreQuatorze femmes. Dix pays. Une mission commune : rendre les données de genre visibles et renforcer la démocratie.
InWanaData AfricabyWanaData·May 18WanaData EPD-DDI Fellowship Cohort II: Advancing the Visibility of Gender DataFourteen women. Ten countries. One shared mission: to advance the work of making gender data visible and democracy stronger.
APARNA KOTAKONDA·May 12GCP-PDE — 30 Practice Questions to Crack Section 4 — Preparing and using data for analysisVisualisation. ML. Sharing. No repeats. No mercy.
APARNA KOTAKONDA·May 9GCP-PDE — Teaching the Machine to Think Like Cymbal — Preparing Data for AI and ML (Section 4.2)A machine learning model is only as good as the data it was trained on. Garbage in, garbage predictions out.
APARNA KOTAKONDA·May 4GCP-PDE — Designing the Warehouse Before You Fill It — Planning for a Data Warehouse (Section 3.2)A data warehouse without a data model is just an expensive filing cabinet with the files thrown on the floor.
