AI & Data
Computer Vision Challenges
Computer Vision challenges put you to work teaching machines to see. You'll develop skills in Image Processing and CNN Classification, build pipelines with OpenCV, tackle Object detection and Segmentation, and adapt pretrained models through Transfer learning.
From there you'll handle the harder edges — Custom architectures, 3D vision, Real-time inference, and Computer Graphics — building and deploying vision systems the way applied research teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - CodeIntermediateNew
De-Identify Patient Images for a Pharma Research Pipeline
You receive 500 internal benchmark images (already cleared for use), each labelled with bounding boxes around face/tattoo/jewelry regions. Build a pipeline that detects these re…
- Image De Identification
- Object Detection
- Privacy Preserving Vision
Image Processing and Computational Imaging - StrategyBeginnerNew
Revamping Loyalty for a Mexico City D2C Cosmetics Brand
You are hired as a marketing analyst. Analyze Brillo Natural's existing customer data (provided in a simulated dataset) to identify purchase patterns and decision triggers. Prop…
- Consumer Psychology
- Segmentation
- Loyalty Program Design
Open coursework - CodeIntermediateNew
Defect Detection on PCBs for a Hardware-AI Manufacturer
Use the publicly-available PCB defect dataset (e.g., DeepPCB or HRIPCB). Fine-tune a small object detector (YOLOv8n or RT-DETR-small) on the 6 defect classes. Evaluate mean Aver…
- Object Detection
- Transfer Learning
- Model Evaluation
Computer Vision 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
- StrategyIntermediateNew
Redesign TaskFlow's Pricing to Win Enterprise Without Losing Small Businesses
Working only from the materials provided to you — the customer accounts and usage dataset (taskflow-accounts-usage), the current pricing and packaging brief (taskflow-pricing-br…
- Pricing Strategy
- Segmentation
- Financial Analysis
Open coursework - CodeBeginnerNew
Semantic Segmentation for a Solar-Panel Inspection Drone
Use a publicly-available solar-panel dataset (or the PV-Defect-Detection dataset). Fine-tune a small U-Net or SegFormer-tiny on panel/no-panel pixel-level segmentation. Evaluate…
- Semantic Segmentation
- Cnn
- Transfer Learning
Open coursework - CodeSeniorNew
Train a 3D Object Detector for Highway Trucking
Use the nuScenes or Waymo Open Dataset (open access) as your training and evaluation source. Fine-tune a strong baseline (e.g., CenterPoint or BEVFusion) and define an evaluatio…
- 3d Object Detection
- Perception
- Pytorch
AI for Autonomous Vehicles - CodeFoundationalNew
Classify Retail Product Photos for an E-Commerce Marketplace
Use a publicly-available product-image dataset (e.g., Fashion-MNIST extended, or a Kaggle e-commerce subset of around 10k images across 12 categories). Fine-tune a small pretrai…
- Image Classification
- Transfer Learning
- Pytorch
Open coursework - 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.
- ResearchSeniorNew
Pretrain a Small Vision Transformer with Self-Supervised Learning
You receive 80,000 unlabeled 224x224 histology tiles plus 4,000 labeled tiles split into train/val/test. Pretrain a ViT-Small using a self-supervised method of your choice (DINO…
- Self Supervised Learning
- Vision Transformers
- Pytorch
Advanced Deep Learning - CodeBeginnerNew
Build a Face-Anonymization Tool for a Civic-Tech Newsroom
Use a pretrained face detector (RetinaFace or YOLOv8-face is fine). Build a Python tool with a Gradio or Streamlit UI that: (1) detects faces in an uploaded photo, (2) shows det…
- Object Detection
- Image Processing
- Opencv
Open coursework - DesignIntermediateNew
Rescue a 3x3 Convolution Accelerator on Zynq UltraScale+
Working only from the materials provided, reach and defend an engineering judgment, then deliver the working accelerator that proves it. Read the engineering case file ('vision-…
- Systemverilog
- Fpga
- Axi Stream
Open coursework - AnalysisBeginnerNew
Cluster a Telco's Subscriber Base for a Pricing Refresh
You receive 12 months of anonymized subscriber-level data: monthly minutes, SMS, mobile data, top-up frequency, top-up amount, churn flag, and tenure. Clean and feature-engineer…
- Clustering
- Feature Engineering
- Exploratory Data Analysis
Data Mining and Knowledge Discovery 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
- CodeIntermediateNew
Domain-Adapt an NLP Pipeline from News to Customer-Support Tickets
You receive 30,000 anonymized customer-support tickets (PT-BR + ES) plus the news-trained NER and intent models. Apply continued pretraining of a multilingual encoder (e.g., XLM…
- Transfer Learning
- Domain Adaptation
- Continued Pretraining
Open coursework - ResearchIntermediateNew
Multi-Task Learning for a Healthtech Triage Model
You receive 40,000 anonymized de-identified intake-form records with two labels: urgency tier (4 classes) and routed sub-specialty (12 classes). Train (1) two independent classi…
- Multi Task Learning
- Transfer Learning
- Transformer
Open coursework - AnalysisIntermediateNew
Transfer-Learning Backbone Bake-Off for Retail Product Tagging
You receive 80,000 retail product images tagged with multiple labels from a 250-tag taxonomy. Use each of the three pretrained backbones via two transfer strategies: (1) linear …
- Transfer Learning
- Fine Tuning
- Self Supervised Learning
Open coursework - CodeBeginnerNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- Image Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision - CodeBeginnerNew
Team Practicum: Build a Crop-Disease Classifier with a Field Partner
You receive a labeled dataset of about 8,000 phone photos plus around 1,200 unlabeled photos from a held-out county. Audit and clean the labels (expect 5-10% noise), train a Mob…
- Transfer Learning
- Pytorch
- Model Evaluation
Open coursework - CodeIntermediateNew
Triage Medical-Imaging Annotations with a Small Vision Model
Train a binary normal/abnormal classifier on the public CheXpert or NIH ChestX-ray14 dataset. Use temperature scaling to calibrate the output, then define abstention thresholds …
- Image Classification
- Transfer Learning
- Calibration
Applied Machine Learning - AnalysisBeginnerNew
Sales Performance Analysis for a 40-Person SaaS Scale-Up
You will receive a dataset containing 500+ sales opportunities with fields like deal value, stage, source, close date, and account size. Your challenge is to design a data mart …
- Data Warehousing
- Etl
- Olap
Open coursework - CodeIntermediateNew
Few-Shot Defect Classifier for a Fast-Onboarding Industrial AI Vendor
You receive a multi-customer defect dataset (8 historical customers, 4-6 defect classes each). Treat 6 customers as the meta-training set and 2 as the held-out 'new customer' sc…
- Meta Learning
- Few Shot Learning
- Prototypical Networks
Open coursework - CodeSeniorNew
Video Action Recognition for a Retail Loss-Prevention Startup
Use a public action-recognition dataset (UCF101 + a small curated retail-action subset; the latter is provided synthetic or you can label 50 short clips). Fine-tune a small back…
- Video Understanding
- Action Recognition
- Transfer Learning
Computer Vision - ResearchIntermediateNew
Reproduce a Vision-Model Paper Under a Reproducibility Standard
Pick a vision-model paper from CVPR or NeurIPS 2024-2025 with publicly available code and a manageable compute footprint (single-GPU under 24 hours). Reproduce the headline metr…
- Reproducibility
- Experimental Design
- Model Evaluation
AI Measurement and Evaluation - CodeIntermediateNew
Train an Object Detector for an Autonomous-Forklift Robotics Startup
You receive 12,000 labeled warehouse images (pallets, pedestrians, forklifts) plus a 1,500-image safety-test set heavy on pedestrian edge cases. Train an object detector (YOLOv8…
- Object Detection
- Yolo
- Edge Deployment
Deep Learning for Computer Vision - ResearchBeginnerNew
Drug-Repurposing Candidate Screen with Embedding Similarity
You receive (1) a list of 15 known therapeutic candidates (SMILES + ChEMBL identifiers) for a single rare disease, (2) a database of about 4,500 marketed drugs (SMILES + ATC cod…
- Molecular Embeddings
- Similarity Search
- Transfer Learning
Machine Learning for Healthcare and Biomedicine
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.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.