Reinforcement Learning MLOps with Ray Virtual Internship
In this advanced virtual internship, students will learn how to implement MLOps best practices for training, monitoring, and deploying reinforcement learning models using the Ray framework. They will gain hands-on experience with tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes to build robust and scalable ML pipelines. By the end of the internship, students will be able to effectively manage the entire lifecycle of RL models in a production environment.
Track Overview
Tasks & Milestones
Explore Ray Fundamentals
AdvancedIn this task, students will get hands-on experience with the Ray framework, including setting up a development environment, running basic Ray programs, and understanding the core concepts of the framework.
Train and Tune an RL Model with Ray RLlib
AdvancedIn this task, students will use Ray RLlib to train and tune a reinforcement learning model for a classic control problem, such as the CartPole environment.
Deploy an RL Model with Ray Serve
AdvancedIn this task, students will use Ray Serve to deploy a trained RL model, exploring techniques for scaling, load balancing, and monitoring the deployment.
Implement a CI/CD Pipeline for RL Model Deployment
AdvancedIn this task, students will use Kubeflow and Airflow to build a CI/CD pipeline for versioning, testing, and deploying their RL models.
Prerequisites
- • Proficiency in Python programming
- • Experience with machine learning and reinforcement learning concepts
- • Familiarity with Docker and Kubernetes
Certificate
Certificate of Completion
Earn a certificate upon successful completion