Simulation Details

Bytek is a leading technology company specializing in scalable AI-driven solutions for industries like retail, fintech, and logistics. The company excels in developing independent micro-services that integrate seamlessly into larger systems, using tools like FastAPI for API development, Docker for containerization, and Uvicorn for deployment. Bytek has demonstrated its ability to handle high-traffic environments, such as processing up to 10 million requests per second, and has successfully implemented predictive analytics solutions that drive measurable business outcomes. Collaboration is central to Bytek’s success, with teams of machine learning engineers, data scientists, and DevOps specialists working together to deliver innovative solutions that balance technical performance with strategic objectives.

In the simulation “Propensity Micro-service Prototype,” you will take on the role of a Machine Learning Engineer tasked with designing and defending a predictive micro-service prototype. Your goal is to forecast user propensity for specific actions, such as purchasing within 30 days, while navigating conflicting stakeholder priorities and tight deadlines. The micro-service must process up to 10 million requests per second, maintain a predictive accuracy threshold of at least 90%, and adhere to Bytek’s operational standards.

Your mission involves three key tasks:
1. Design the predictive logic and API interface: Implement a POST endpoint using FastAPI that accepts user attributes and returns a propensity score. You will need to choose a predictive model, define a JSON schema for input validation, and ensure robust error handling mechanisms.
2. Implement error handling and validation mechanisms: Develop mechanisms to validate input data and handle edge cases, ensuring the service operates reliably under various conditions.
3. Document the micro-service for deployment: Create comprehensive deployment documentation, including containerization steps, health-check endpoint configuration, and operational guidelines.

Throughout the simulation, you will engage in real-time discussions with Bytek’s stakeholders:
– Susan Delgado (AI Solutions Manager): Discuss scalability requirements and ensure alignment with Bytek’s growth objectives.
– Ridhi Nair (Lead Data Scientist): Validate the predictive model’s accuracy and receive feedback on its design.
– Aaron Nichols (DevOps Specialist): Address deployment readiness and refine documentation to meet Bytek’s operational standards.

Your ability to balance technical trade-offs, clarify ambiguous requirements, and respond to stakeholder feedback will be critical to delivering a solution that meets Bytek’s high standards for scalability, predictive accuracy, and operational clarity.

To complete the simulation, you need to:
– Design and implement a POST endpoint using FastAPI that accepts user attributes and returns a propensity score.
– Develop robust error handling and input validation mechanisms using Pydantic.
– Document the micro-service for deployment, including containerization steps, health-check endpoint configuration, and operational guidelines.
– Engage in real-time discussions with Susan Delgado, Ridhi Nair, and Aaron Nichols to address their priorities and refine your solution.

Once you complete all the tasks, save the required final document in a PDF or DOCX and send it to Susan Delgado, the AI Solutions Manager.

Team

Who you will work with in this Simulation
Your team is 100% generated by AI – you will not interact with real people and no human will read your conversation.

Alex Taylor
Machine Learning Engineer

Susan Delgado
AI Solutions Manager

Ridhi Nair
Lead Data Scientist

Aaron Nichols
DevOps Specialist

Organization

A technology company specializing in scalable AI-driven solutions for industries such as retail, fintech, and logistics.