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.
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- CodeIntermediateNew
Build a LangGraph Multi-Agent Researcher
Design the four-agent topology with explicit message contracts. Implement each agent as a separate LLM call with role-specific system prompts, tool access (web search for resear…
- Multi Agent Orchestration
- Langgraph
- LLM Tool Use
Multi-Agent Systems - ResearchIntermediateNew
Audit an Agentic Workflow for Safety Failures
Read the system's existing capability spec + tool-allow-list. Design 50+ adversarial inputs across categories: prompt-injection, tool-confusion, scope-escape (agent does somethi…
- Ai Red Teaming
- Agent Safety
- Prompt Injection
Multi-Agent Systems - CodeIntermediateNew
Build a Vision-Language Search for an E-commerce Catalog
Pick a vision-language encoder (OpenCLIP, SigLIP, or BLIP-2 image-text variant). Index all 600k product images into a vector database (Qdrant/FAISS). Build a query-time pipeline…
- Vision Language Models
- Clip
- Vector Search
Open coursework - CodeIntermediateNew
Train a Multimodal Classifier for Medical Triage
Pick a fusion architecture (early fusion via cross-attention, late fusion via score combination, or a unified multimodal encoder like FLAVA/CoCa). Train on the 14,000 pairs with…
- Multimodal Fusion
- Cross Attention
- Pytorch
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- CodeIntermediateNew
Build an Audio-Visual Speaker Diarization Pipeline
Build the pipeline: face detection + active-speaker detection on video, voice-activity detection + speaker embeddings on audio, then a fusion step that ties tracks to detected f…
- Audio Visual Fusion
- Speaker Diarization
- Active Speaker Detection
Multimodal Machine Learning - ResearchIntermediateNew
Fine-Tune a Vision-Language Model for Image Captioning
Take BLIP-2 or LLaVA-1.6 as the base. Fine-tune (LoRA is fine) on a 4,000-image accessibility-curated dataset where each image has a useful caption written by a low-vision-exper…
- Vision Language Models
- Lora Fine Tuning
- Pytorch
Multimodal Machine Learning - CodeIntermediateNew
Build a Multilingual Customer-Email Classifier
You receive 28,000 labeled emails (skewed toward English and Mandarin). Try at least two approaches: (1) a fine-tuned multilingual transformer (XLM-RoBERTa or mDeBERTa) and (2) …
- Text Classification
- Multilingual NLP
- Transformers
Open coursework - CodeIntermediateNew
Build a Domain-Specific Named-Entity Recognizer for Legal Contracts
Start from a strong English NER base (spaCy transformer or LegalBERT). Fine-tune on a provided 1,200-contract labeled dataset for the 9 entity types. Handle long contracts (ofte…
- Named Entity Recognition
- Sequence Labeling
- Domain Adaptation
Open coursework - Browse challenges
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Pricing Strategist
Set the price that captures value without leaving sales on the table. Demand modelling, willingness-to-pay research, and the disciplined experimentation that turns pricing into a competitive advantage.
- CodeIntermediateNew
Adapt Machine Translation to a Niche Domain
Pick an open MT base (NLLB-200 or a strong open M2M model). Build a parallel corpus of around 8,000 sentence pairs from the company's bilingual safety standards. Fine-tune on th…
- Machine Translation
- Domain Adaptation
- Transformers
Natural Language Processing - CodeIntermediateNew
Parse and Structure Clinical Discharge Summaries
Combine traditional NLP (section segmentation, sentence parsing) with LLM extraction (small open model + structured-output enforcement). Build the pipeline so every extracted fi…
- Structured Extraction
- Clinical NLP
- Parsing
Open coursework - CodeIntermediateNew
Build a Small Transformer from Scratch and Train It on Code
Implement multi-head self-attention, RMSNorm, rotary positional embeddings, and a causal LM head from scratch — no Hugging Face shortcuts for the model code (you may use Hugging…
- Transformers
- Self Attention
- Pytorch
Neural Networks for NLP - CodeIntermediateNew
Fine-Tune a Sequence-to-Sequence Model for Code-Doc Generation
Take a small base model (CodeT5+ or a distilled CodeLlama-Instruct). Build the dataset by mining around 8,000 high-quality function-docstring pairs from permissively-licensed Py…
- Seq2seq
- Transformers
- Lora Fine Tuning
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- CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG
- Cross Lingual Retrieval
- Multilingual Embeddings
Open coursework - ResearchIntermediateNew
Probe a Pretrained Encoder for Linguistic Knowledge
Take BERT-base (or DeBERTa-v3-base). Run layer-wise probes across at least 3 linguistic tasks: part-of-speech tagging, dependency arc classification, and semantic role labeling.…
- Interpretability
- Probing
- Transformers
Open coursework - CodeIntermediateNew
Train a Differentially Private Classifier on Medical Records
Use Opacus (PyTorch DP-SGD library). Train a tabular classifier (small MLP + gradient-boosted features) with DP-SGD at the agreed epsilon/delta. Run an accuracy-vs-privacy front…
- Differential Privacy
- Dp Sgd
- Opacus
Open coursework - CodeIntermediateNew
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
Open coursework - StrategyIntermediateNew
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
Open coursework - CodeIntermediateNew
Generate Synthetic Tabular Data with Privacy Guarantees
Implement DP synthetic data generation: either DP-CTGAN, PATE-GAN, or a marginal-based DP method like PrivBayes / MWEM. Train on the real dataset (around 200,000 transactions, 1…
- Synthetic Data
- Differential Privacy
- Generative Models
Privacy-Preserving Machine Learning - CodeIntermediateNew
Diagnose Equipment Failures with a Bayesian Network
You receive 90 days of sensor logs (vibration, spindle temperature, coolant flow, ambient humidity), the maintenance log of 180 failure events labeled by root cause, and a short…
- Bayesian Networks
- Probabilistic Inference
- Parameter Learning
Probabilistic Graphical Models - AnalysisIntermediateNew
Model Patient Pathways with a Hidden Markov Model
You receive de-identified monthly summaries for 8,000 diabetic patients, each row coding the count of primary-care visits, specialist visits, ER visits, new medications, and HbA…
- Hidden Markov Models
- Em Algorithm
- Time Series Modeling
Probabilistic Graphical Models - CodeIntermediateNew
Gaussian Process Regression for Wind Farm Power Curves
You receive 12 months of 10-minute SCADA data (wind speed, air temperature, power output) for 30 representative turbines, plus the manufacturer's published curve. Fit a GP with …
- Gaussian Processes
- Kernel Methods
- Uncertainty Quantification
Open coursework - AnalysisIntermediateNew
MCMC for Conversion-Funnel A/B Testing at a Marketplace
You receive 6 weeks of per-visitor funnel data (visit, sign-up, trial start, trial-to-paid conversion) split by variant and by acquisition channel (organic, paid social, paid se…
- Mcmc
- Bayesian Hierarchical Models
- Ab Testing
Open coursework - CodeIntermediateNew
Variational Autoencoder for Synthetic Tabular Banking Data
You receive a 500K-row anonymized transaction dataset with 25 columns (mixed numerical + categorical). Train a VAE (TabVAE or a small custom model) with appropriate likelihoods …
- Variational Inference
- Deep Generative Models
- Synthetic Data
Open coursework - CodeIntermediateNew
Prompt-Injection Hardening for a Customer-Support Agent
You receive the current agent prompt, the pen-tester's 60-attack injection test set (direct prompt injection, indirect via doc content, refusal-bypass, and exfiltration), and a …
- Prompt Injection Defense
- System Prompt Design
- Red Teaming
Prompt Engineering
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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
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Step 06
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