Information Technology Challenges
Explore information technology challenges on Ewance to develop skills companies are actively hiring for. Work on briefs covering cloud, infrastructure, security, and platform engineering.
Most Popular
- CodeIntermediateNew
Train a Sequence Model for Wearable-Telemetry Sleep Staging at a Healthtech
You receive 220 nights of wearable telemetry from 60 subjects with PSG ground-truth labels. Train three sequence models: an LSTM baseline, a 1D-CNN+GRU hybrid, and a small trans…
- Sequence Models
- Lstm
- Hugging Face Transformers
Deep Learning - CodeIntermediateNew
Fine-Tune a Transformer for Customer-Support Triage at an Enterprise AI Vendor
You receive 240,000 labeled support tickets across 14 queues, with English, Bahasa Indonesia, and Tagalog. Fine-tune a multilingual transformer encoder (XLM-RoBERTa-base is a st…
- Hugging Face Transformers
- Fine Tuning
- Multilingual NLP
Deep Learning - CodeIntermediateNew
Quantize a CNN for Battery-Powered Wildlife Cameras at a Climate Nonprofit
You receive an FP32 CNN (MobileNetV2 fine-tuned to 22 species, around 13 MB) and a hold-out test set of 4,000 images. Quantize to int8 (post-training quantization first, then qu…
- Quantization
- Qat
- Edge Deployment
Deep Learning - CodeIntermediateNew
Train a Deep Q-Network for Warehouse Robot Routing
You receive a Gymnasium-compatible warehouse simulator (50x50 grid, 8 dynamic obstacle pedestrians, 20 randomized pick locations) and a baseline A* planner script. Train a DQN a…
- Deep Q Learning
- Reinforcement Learning
- Pytorch Or Tensorflow
Deep Reinforcement Learning Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- ResearchIntermediateNew
Tune a PPO Policy for an Energy-Storage Trading Bot
You receive 18 months of 15-minute Nordic spot-price data, a battery dynamics model (capacity, round-trip efficiency, degradation curve), and a rule-based baseline that earns ab…
- Policy Gradients
- Ppo
- Reinforcement Learning
Deep Reinforcement Learning - CodeIntermediateNew
Use Actor-Critic to Auto-Tune a HVAC Control Policy
You receive a Sinergym wrapper around the EnergyPlus model of one floor with 8 thermal zones, weather data for one year, and occupancy schedules. Train a Soft Actor-Critic (SAC,…
- Actor Critic
- Soft Actor Critic
- Continuous Control
Deep Reinforcement Learning - AnalysisIntermediateNew
Imitation Learning from Human Demos for a Drone Inspection
You receive 6 hours of expert pilot demonstrations (state-action pairs at 20 Hz) recorded in an AirSim wind-farm environment with 3 turbine designs, plus a held-out 4th turbine …
- Imitation Learning
- Behavioral Cloning
- Dagger
Deep Reinforcement Learning - CodeIntermediateNew
Prune and Distill a Speech Model for a Hearable
You receive a trained 280 KB CNN keyword spotter (10 keywords + silence + unknown) with 96.1% top-1 accuracy on the Google Speech Commands test set. Apply structured pruning (ch…
- Pruning
- Knowledge Distillation
- Model Compression
Edge ML and On-Device Machine Learning - 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.
- AnalysisIntermediateNew
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 Optimization
Edge ML and On-Device Machine Learning - ResearchIntermediateNew
Hardware-Aware NAS for a Wearable ECG Classifier
You receive a labeled subset of an arrhythmia ECG dataset (about 80,000 10-second windows, 4 classes), a microcontroller latency lookup table (op-level milliseconds) for a Corte…
- Neural Architecture Search
- Hardware Aware Design
- Edge Inference
Edge ML and On-Device Machine Learning - CodeIntermediateNew
Solve a Vehicle-Routing Problem with Tabu Search
You receive a week of anonymized daily VRPTW instances (around 800 orders per day, 120 vehicles, hard delivery windows). Implement tabu search with: a route-insertion constructi…
- Tabu Search
- Metaheuristics
- Vehicle Routing
Evolutionary Computation and Metaheuristic Search - DesignIntermediateNew
NSGA-II Multi-Objective Design for an Edtech Curriculum Recommender
You receive a synthetic student cohort (300 students with skill profiles), a content catalog (around 1,200 items with difficulty + duration + topic tags), and a simulator that e…
- Nsga Ii
- Multi Objective Optimization
- Metaheuristics
Evolutionary Computation and Metaheuristic Search Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- ResearchIntermediateNew
Build Saliency-Map Explanations for Dermatology Triage
You receive a trained CNN (ResNet-50 backbone, 7-class lesion classifier) and a 1,000-image held-out test set with dermatologist labels. Implement Integrated Gradients, GradCAM,…
- Saliency Maps
- Integrated Gradients
- Gradcam
Explainable and Interpretable AI - DesignIntermediateNew
Counterfactual Explanations for an Insurance Pricing Model
You receive a trained LightGBM regression model (premium in GBP), the feature schema (28 features, 14 mutable from the customer's side), and 500 sample quotes. Use DiCE (Diverse…
- Counterfactual Explanations
- Dice Ml
- Interpretability
Explainable and Interpretable AI - CodeIntermediateNew
LoRA Fine-Tune a 7B LLM for Legal-Clause Extraction
You receive a curated extraction dataset (2,000 train, 500 val, 500 test contracts with span-level labels across 12 clause types) and a fine-tunable 7B base model (e.g., Llama-3…
- Fine Tuning
- Fine Tuning
- Parameter Efficient Tuning
Fine-Tuning Large Language Models - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG Architectures
Fine-Tuning Large Language Models - CodeIntermediateNew
Instruction-Tune a Small Model for an Edtech Tutor
You receive a 1.5B base model (e.g., SmolLM-1.7B or Qwen-1.8B), permission to use 2 hours of a rented A100, and a curated seed of around 5,000 math-tutoring dialogues. Augment w…
- Instruction Tuning
- Fine Tuning
- Dataset Curation
Fine-Tuning Large Language Models - AnalysisIntermediateNew
Catastrophic-Forgetting Audit on a Domain Fine-Tune
You receive the fine-tuned 7B chemistry model and its base, plus a benchmark basket (MMLU subset, GSM8K, IFEval, a small instruction-following set). Run all 4 benchmarks on both…
- Catastrophic Forgetting
- LLM Evaluation
- Fine Tuning
Fine-Tuning Large Language Models - DesignIntermediateNew
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 - ResearchIntermediateNew
Neuro-Symbolic Question Answering on an Enterprise Knowledge Graph
You receive a curated Turtle-format knowledge graph (around 2 million triples covering organizational structure, products, projects), 200 labeled question-SPARQL pairs split 140…
- Neuro Symbolic
- Sparql
- Knowledge Graphs
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - CodeIntermediateNew
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 - CodeIntermediateNew
Fine-Tune a Diffusion Model for an E-commerce Product Studio
You receive 1,200 curated product + lifestyle images across 6 product categories, a brand-style guide, and the company's current studio cost per image (around EUR 18). Fine-tune…
- Diffusion Models
- Stable Diffusion
- Dreambooth
Generative AI - CodeIntermediateNew
Build a Multimodal Generation Pipeline for a Tourism Operator
You receive 40 sample 30-second videos shot by tour guides, the operator's brand voice doc, and SEO keyword lists for EN/PT/ES. Build a pipeline that (1) extracts a representati…
- Multimodal Generation
- Vision Language Models
- LLM Inference
Generative AI - ResearchIntermediateNew
Evaluate VAEs vs. Diffusion for Synthetic Tabular-Data Generation
You receive a real labeled dataset (around 18,000 anonymized patient records, 32 features, binary outcome) and the team's existing VAE baseline. Train a tabular diffusion model …
- Tabular Diffusion
- Vae
- Synthetic Data
Generative AI
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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Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.