Practice Project
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Sandboxes
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Lab Type : Nuvepro’s Practice Project
Problem Statement : This practice project is designed to evaluate practical and conceptual understanding of enterprise AI validation, monitoring, and quality engineering workflows. Participants will work on AI-driven quality assurance and observability use cases focused on Large Language Models (LLMs), automated testing, and performance optimization. The practice project includes the following areas:
- LLM Telemetry, Regression Testing and Performance Monitoring: Implement monitoring and evaluation mechanisms to analyze LLM behavior, track performance metrics, identify regressions, and ensure system reliability across AI workflows.
- Automated Defect Analysis, Validation and Quality Metrics Reporting: Build intelligent validation and reporting workflows capable of detecting defects, validating AI-generated outputs, and generating actionable quality metrics for enterprise-scale AI systems.
Customer Segment : A Global IT Services, Consulting, and Digital Transformation Organization.
Lab Type : Nuvepro’s Sandboxes
Tools Delivered : AWS Account Labs
Customer Segment : A Global IT Training, Certification, and Cloud Learning Solutions Provider.
Lab Type : Nuvepro’s Sandboxes
Tools Delivered : Chrome, Git, PyCharm, VS Code with Python, LangChain, Anaconda Python, LibreOffice, Firefox
Customer Segment : A Global Payments Technology Organization.
Lab Type : Nuvepro’s Sandboxes
Tools Delivered : Ubuntu 22 Desktop with STM Cube IDE
Customer Segment : A Global Technology and Engineering Services Organization.
Lab Type : Nuvepro’s Sandboxes
Tools Delivered : Chrome, Git, PyCharm, VS Code with Python, LangChain, Anaconda Python, LibreOffice, Firefox
Customer Segment : A Global Payments Technology Organization.