MLOps for Time Series Forecasting Virtual Internship
In this virtual internship, students will learn how to develop and deploy time series forecasting models using MLOps best practices. They will gain hands-on experience with tools like MLflow, Kubeflow, and Airflow to build, monitor, and maintain their models in a production environment. By the end of the internship, students will be able to apply MLOps principles to their own time series forecasting projects and be well-equipped for a career in machine learning engineering or operations.
Track Overview
Tasks & Milestones
Explore MLOps Principles and Challenges
IntermediateIn this task, students will research and summarize the key principles of MLOps and the common challenges in deploying time series forecasting models.
Implement Time Series Forecasting with MLflow
IntermediateIn this task, students will use MLflow to develop, track, and package a time series forecasting model.
Deploy Time Series Forecasting Model with Kubeflow
IntermediateIn this task, students will use Kubeflow to deploy a time series forecasting model and set up model monitoring and drift detection.
Implement End-to-End MLOps Workflow with Airflow
IntermediateIn this task, students will use Airflow to build an automated MLOps workflow for their time series forecasting model.
Prerequisites
- • Python programming
- • Machine learning fundamentals
- • Time series analysis
Certificate
Certificate of Completion
Earn a certificate upon successful completion