Distributed Systems
If you like applying Distributed Systems, every challenge here gives you a chance to practice it on a real industry brief.
- CodeAdvancedNew
Scale Feature Pipelines for a Hyperscaler Search-Ranking Team
You receive a synthetic-but-realistic 80 GB sample of the ranking events plus the existing Spark pipeline (PySpark) and a Spark UI snapshot from a recent production run. Profile…
- Spark
- Distributed Systems
- Performance Profiling
Machine Learning at Scale - CodeAdvancedNew
Build a Federated Learning Prototype Across Two Hospitals
Simulate two sites with non-IID data splits (one site skews older, the other younger). Implement FedAvg using Flower (or PySyft). Run for at least 50 communication rounds; repor…
- Federated Learning
- Fedavg
- Secure Aggregation
Privacy-Preserving Machine Learning - CodeAdvancedNew
Implement Bulk Synchronous Parallel PageRank on a 1.5B-Edge Graph
Choose either Apache Spark + GraphX (Pregel API) or a vanilla MPI + C++ implementation. Run 25 iterations of PageRank on the 1.5B-edge graph (graph file format provided: CSR par…
- Parallel Algorithms
- Bsp
- Graph Algorithms
Parallel and Distributed Algorithms
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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