AI/ML Infrastructure and MLOps Pipeline
Build and operationalize machine learning pipelines with automated training and deployment.
About This Assessment
AI infrastructure demands are exploding with job postings requiring AI skills nearly doubling from 5% in 2024 to over 9% in 2025. MLOps engineers command premium salaries as companies race to productionize AI. This assessment validates ability to build ML pipelines, manage model lifecycle, and deploy models to production with monitoring and retraining capabilities.
What Candidates Will Do
Build an automated ML training pipeline that versions datasets, tracks experiments, and stores models
Deploy a trained model as a scalable API endpoint with health checks and performance monitoring
Implement model drift detection and automated retraining triggers
Automated Grading
Verify ML pipeline successfully trains a model with experiment tracking, confirm model is deployed and responding to API requests, validate that monitoring captures prediction latency and drift metrics, check that versioning is properly implemented
Environment
Ubuntu 22.04 with Python 3.10+, Docker, kubectl, and ML tools (MLflow, Kubeflow, or similar) pre-installed. Provide Kubernetes cluster with GPU node or CPU with sufficient resources
Ready to prove your skills?
Purchase this assessment and get started today.
$179.00