- CodeAdvancedNew
Description-Logic Reasoner for Insurance-Policy Coverage Checks
You receive 50 representative coverage rules in plain English (from the current rule engine) and a sample of 1,000 anonymized claim cases with the current engine's outcomes (cov…
- Description Logics
- Owl
- Reasoning
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - DesignAdvancedNew
Design Hybrid Search for an E-Commerce Product Catalog
You receive 80,000 anonymized product records (title, description, category, attributes) and a sample of 30,000 search log entries with click-through labels. Embed the catalog w…
- Hybrid Search
- Embedding Models
- Bm25
Vector Databases and Embeddings - DesignAdvancedNew
Build an OWL Ontology for a Pharma R&D Knowledge Base
You receive a CSV-form starter knowledge base (around 4,000 compounds, 600 targets, 1,200 assays) and a list of 12 competency questions the scientists currently can't answer wit…
- Ontology Design
- Owl
- Knowledge Representation
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - DesignAdvancedNew
Design a Customer 360 Graph for a Cross-Border Fintech
You receive 500 sample customer records across CRM, payments core, and KYC systems, plus a 50-record entity-resolution benchmark (pairs labelled same/different). Design an OWL o…
- Customer 360
- Entity Resolution
- Owl Ontology
Knowledge Graphs and Semantic Web Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- PresentationAdvancedNew
Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo
Design a hybrid agent for a 'company-policy assistant' demo: a symbolic planner decomposes user goals into typed subtasks ('find policy', 'check applicability', 'compose answer'…
- Hybrid Ai
- Symbolic Planning
- Retrieval Augmented Generation
Artificial Intelligence: Principles and Techniques - StrategyAdvancedNew
Design a Post-Editing Workflow for a Cross-Border Fintech
You will design a 4-stage MTPE workflow: (1) source-content readiness check, (2) MT generation with the existing vendor, (3) post-editing with tier-based effort (light vs. full)…
- Mt Evaluation
- Workflow Design
- Neural Mt
Machine Translation - DesignAdvancedNew
Build a Multi-Region Online Inference Service with SLAs
Design the topology: model artifact storage, regional inference fleets (Triton, vLLM, or BentoML), traffic router, observability stack (Prometheus + Grafana). Pick a rollout str…
- Inference Serving
- Multi Region Deployment
- Kubernetes
Machine Learning Systems - DesignAdvancedNew
Design a Lab-Automation Pipeline for a Bangalore Materials Startup
Design (not build) the full closed-loop lab pipeline: data layer (LIMS plus experiment store), model layer (a surrogate plus an acquisition function such as Expected Improvement…
- Systems Architecture
- Active Learning
- Mlops Design
AI for Science and Engineering - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- StrategyAdvancedNew
Design a PETs Strategy for an EU AI Act Use Case
Map the underwriting use case to applicable PETs across the data-lifecycle stages (training, evaluation, inference, monitoring). For each, document: privacy property gained, acc…
- Pets Strategy
- Differential Privacy
- Federated Learning
Privacy-Preserving Machine Learning - AnalysisAdvancedNew
Benchmark NPUs for an Autonomous Forklift Vision Stack
You receive ONNX exports of the 3 production models, a labeled validation set of 2,000 forklift-camera frames, and developer-kit access to three NPU candidates (anonymized as NP…
- Edge Inference
- Npu Benchmarking
- Onnx
Edge ML and On-Device Machine Learning
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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