Computer Science
Cloud & Infrastructure Challenges
Cloud & Infrastructure challenges put you in charge of the foundations every service runs on. You'll work through Cloud Computing fundamentals on AWS or Azure, provision environments with Terraform, control access with Cloud IAM, and ship workloads via Helm + Kubernetes deployments.
From there you'll handle the harder edges — Multi-cloud architecture, Landing zones, Advanced IaC modules, FinOps & cost optimization, and the AWS Well-Architected Framework — designing infrastructure the way platform teams actually scale it. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· FinOps & cost optimization Clear- AnalysisIntermediateNew
Cost-Optimize a Misshapen Kubernetes Cluster
Receive 30 days of cluster metrics (Prometheus + AWS Cost Explorer exports), Helm releases, and PodDisruptionBudgets per namespace. Profile: identify the top 3 cost drivers (lik…
- Kubernetes Orchestration
- Finops & Cost Optimization
- AWS Or Azure
Cloud Computing - CodeSeniorNew
Cost-Optimize a 24/7 LLM API Cluster
Profile the current usage (24-hour trace, per-team breakdown). Pick a cost-optimization mix from: time-based autoscaling, spot/preemptible instances with graceful drain, smarter…
- LLM Serving
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ML Engineering and Production ML - AnalysisIntermediateNew
Cut Latency and Cost on a High-Volume Summarization Service
You receive 30 days of anonymized request logs (prompt token counts, completion token counts, latencies, models used). Profile the cost and latency distribution, then design and…
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- Latency Optimization
- Prompt Compression
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Cardinality and Cost Control on a Datadog-Based Gaming Platform
Receive an anonymized export of Datadog metric inventory (metric names, tag-cardinality counts, monthly cost per metric, team owner). Identify the top 30 cost-driver metrics, cl…
- Cardinality Control
- Datadog
- Finops & Cost Optimization
Software Observability 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
- AnalysisBeginnerNew
Cost-Optimize an Embedding Pipeline for a Customer Support Knowledge Base
You receive: (a) the current pipeline (full re-embed on any article change, OpenAI text-embedding-3-large, 3,072 dims) with one month of cost logs, (b) a sample of 5,000 article…
- Embedding Models
- Finops & Cost Optimization
- Change Detection
Vector Databases and Embeddings - AnalysisIntermediateNew
Cost-Profile a Spark Job at Scale and Cut the Bill in Half
Receive the PySpark job (around 1,800 lines), 5 nights of Spark UI + EMR metrics, and the EMR cluster config. Profile to find the top 3 cost drivers (likely candidates: skewed j…
- Apache Spark
- Finops & Cost Optimization
- Etl Pipelines
Big Data and Data-Intensive Systems - AnalysisBeginnerNew
Right-Size a Real-Time Recommendation Serving Cluster
You receive 7 days of request-level telemetry (timestamp, latency, error code, pod) plus the existing Horizontal Pod Autoscaler (HPA) and node-group configs. Analyze traffic pat…
- Model Serving
- Kubernetes Orchestration
- Autoscaling
Machine Learning at Scale
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.
Related skill families
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