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
Build an End-to-End ML Pipeline for Loan-Default Prediction
You receive 24 months of historical application + outcome data (about 380,000 rows). Build a pipeline using a workflow orchestrator (Prefect, Kedro, or a simple Makefile chain) …
- Ml Pipelines
- Feature Engineering
- Pipeline Testing
Machine Learning in Practice - AnalysisIntermediateNew
Run a Pre-Deployment Fairness + Drift Audit on a Hiring Model
You receive a trained classifier (joblib), the training data sample, and a held-out 'next-month' evaluation set. Compute group fairness metrics (false-positive-rate gap, true-po…
- Fairness Metrics
- Drift Detection
- Bias Mitigation
Machine Learning in Practice - AnalysisIntermediateNew
Compare Kernel SVMs and Gradient Boosting on Imbalanced Tabular Data
You receive a 220,000-row anonymized loan-default dataset with mixed numeric and categorical features and a ~6% positive class. Train and evaluate (1) an RBF-kernel SVM with pro…
- Kernel Methods
- Gradient Boosting
- Model Selection
Machine Learning - CodeIntermediateNew
Build an Ensemble Strategy for Marketing-Mix Modelling
You receive 36 months of weekly marketing-spend and outcome data for 8 sample brands. Build a per-brand baseline gradient-boosting MMM model, then build two more base learners (…
- Ensemble Methods
- Stacking
- Time Series Cv
Machine 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
- CodeIntermediateNew
Build a Sequence Model for Sign-Language Word Recognition
You receive about 12,000 short (1-3s) webcam clips covering a 50-word vocabulary, with body+hand pose features pre-extracted (e.g., MediaPipe Holistic landmarks per frame). Buil…
- Sequence Models
- Hugging Face Transformers
- Pose Estimation
Machine Perception - CodeIntermediateNew
Fine-Tune a Small Transformer for Legal-Domain EN-DE Translation
You receive a 120,000-segment parallel EN-DE legal corpus and a held-out 1,000-segment test set with reference translations. Fine-tune a small pretrained Transformer (e.g., NLLB…
- Neural Mt
- Hugging Face Transformers
- Fine Tuning
Machine Translation - AnalysisIntermediateNew
Audit BLEU vs. COMET on a Multilingual Customer-Support Corpus
You receive 600 source-translation-reference triples covering 6 languages (EN as source; ES/FR/DE/JA/PT-BR/HI as targets), each scored on adequacy and fluency (1-6) by 3 profess…
- Mt Evaluation
- Neural Mt
- Statistical Analysis
Machine Translation - StrategyIntermediateNew
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 - Browse challenges
Explore role
Marketing Analyst
Plan and measure campaigns that grow the business. Funnel analytics, attribution, segmentation, and the rigorous measurement that lets marketing defend its budget at the leadership table.
- 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
Meta-Learning, Transfer Learning, and Multi-Task Learning - 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
- Supervised Learning
Meta-Learning, Transfer Learning, and Multi-Task Learning - 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
- Hugging Face Transformers
Meta-Learning, Transfer Learning, and Multi-Task Learning - 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
Meta-Learning, Transfer Learning, and Multi-Task Learning 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
Build a 30-Day Readmission Risk Model on De-Identified EHR Data
You receive a curated MIMIC-style de-identified EHR cohort (about 28,000 admissions, demographics, comorbidities, labs, prior-admission counts) with 30-day readmission labels. T…
- Ehr Modeling
- Risk Stratification
- Model Calibration
Machine Learning for Healthcare and Biomedicine - ResearchIntermediateNew
Disease-Progression Modelling for a Neurodegeneration Biotech
You receive a curated longitudinal Parkinson's cohort (about 1,200 patients, 4-12 visits each, MDS-UPDRS sub-scores, cognitive assessments, demographics). Fit (1) a linear mixed…
- Disease Progression Modeling
- Mixed Effects Models
- State Space Models
Machine Learning for Healthcare and Biomedicine - AnalysisIntermediateNew
Audit a Sepsis Early-Warning Model for Subgroup Performance
You receive a pre-trained vendor model, the training-data summary, and a held-out hospital-network evaluation set (about 18,000 ICU stays with sepsis labels). Compute AUROC + AU…
- Model Evaluation
- Fairness Metrics
- Model Calibration
Machine Learning for Healthcare and Biomedicine - AnalysisIntermediateNew
Chest-X-Ray Deployment Audit Across Hospital Sites
You receive (1) a vendor-supplied multi-label chest-X-ray classifier, (2) the current single-site held-out evaluation set, (3) a 12,000-image multi-site evaluation set with 14-f…
- Medical Imaging
- Classification
- Model Evaluation
Machine Learning for Imaging and Medical Image Analysis - ResearchIntermediateNew
Hands-on Lab: Reproduce a Recent SOTA Vision Paper
Pick one of three pre-approved 2025 papers (offered by the supervisor) with a known reference codebase you may consult but not copy. Re-implement the model and training loop in …
- Pytorch Or Tensorflow
- Paper Reproduction
- Experimental Design
AI/ML Practicum and Hands-on Lab - AnalysisIntermediateNew
Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
You receive 6 months of production logs (model inputs, predictions, ground truth from chargebacks) plus the original training data and model card. Reproduce the recall drop in a…
- Data Drift Detection
- Model Monitoring
- 5 Whys & Fishbone Root Cause Analysis
AI/ML Practicum and Hands-on Lab - ResearchIntermediateNew
Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Scope the problem yourself (suggested examples: sentiment classification on a niche domain, tabular anomaly detection, time-series forecasting on a public dataset). Define the t…
- Experimental Design
- A/B Testing With Statistical Significance
- Pytorch Or Tensorflow
AI/ML Practicum and Hands-on Lab - DesignIntermediateNew
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 Orchestration
Machine Learning Systems - AnalysisIntermediateNew
Compare ML Compiler Stacks on a Vision Backbone
Take a frozen ResNet-50 (or similar) in ONNX. Compile and benchmark it via TensorRT on Jetson + GPU, ONNX Runtime on all three, OpenVINO on x86 CPU, and IREE on ARM if time allo…
- Ml Compilers
- Tensorrt
- Onnx Optimization
Machine Learning Systems - DesignIntermediateNew
Stand Up a Feature Store for a Series-B Fintech
Pick one priority feature group (recommend the 25 transaction-history features used by the fraud model). Define the offline source-of-truth (likely Snowflake or BigQuery), the o…
- Feature Store
- Feature Engineering
- Airflow Dags
ML Engineering and Production ML - CodeIntermediateNew
Build a Canary Rollout for a Production Recommender
Pick a serving stack (Triton, Seldon Core, KServe, or BentoML). Implement two-model traffic splitting with a configurable percentage (start at 5%). Wire up online metric collect…
- Canary Deployment
- Kubernetes Orchestration
- A/B Testing
ML Engineering and Production ML - DesignIntermediateNew
Instrument a Model Monitoring Stack from Scratch
Pick the priority product (recommend the customer-service RAG assistant, around 40k queries/day). Define monitoring signals: input drift (Evidently/NannyML), output quality (LLM…
- Model Monitoring
- Data Drift Detection
- LLM Evaluation
ML Engineering and Production ML
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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