Reinforcement Learning Ops: Building Robust Production Systems Virtual Internship
In this virtual internship, students will learn how to operationalize reinforcement learning models for production systems. They will gain hands-on experience with model monitoring, drift detection, and model retraining strategies to ensure the robustness and reliability of their RL models. By the end of the internship, students will be equipped with the skills to build and maintain production-ready RL systems.
Track Overview
Tasks & Milestones
Task 1: RL Ops Landscape
AdvancedConduct research on the current state of RL Ops and the key challenges faced by organizations in deploying and maintaining RL models in production.
Task 1: Implement RL Model Monitoring
AdvancedDevelop a model monitoring pipeline for a reinforcement learning model using MLflow and other open-source tools.
Task 2: Analyze RL Model Drift
AdvancedInvestigate and analyze the causes of model drift in a reinforcement learning model deployed in production.
Task 1: Containerize and Deploy RL Models
AdvancedPackage a reinforcement learning model as a Docker container and deploy it to a Kubernetes cluster.
Task 2: Automate RL Model Lifecycle
AdvancedImplement an end-to-end RL model lifecycle management pipeline using Kubeflow and Airflow.
Capstone Project: RL Ops Solution
AdvancedBuild a complete RL Ops solution for a real-world reinforcement learning problem, including model monitoring, drift detection, and automated deployment.
Prerequisites
- • Experience with reinforcement learning algorithms
- • Familiarity with machine learning model deployment
- • Proficiency in Python and software engineering practices
Certificate
Certificate of Completion
Earn a certificate upon successful completion