MLOps for Time Series Forecasting Virtual Internship
In this virtual internship, students will learn how to build end-to-end MLOps pipelines for time series forecasting models. They will gain hands-on experience with data preprocessing, model training, and deployment using tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes. By the end of the internship, students will be able to develop and deploy scalable, production-ready time series forecasting models with robust monitoring and versioning.
Track Overview
Tasks & Milestones
Explore MLOps Concepts
IntermediateIn this task, students will research and summarize the key principles and components of MLOps, as well as its importance in the context of time series forecasting.
Implement a Time Series Data Preprocessing Pipeline
IntermediateIn this task, students will build a data preprocessing pipeline for a time series forecasting problem using Python and popular data processing libraries.
Develop an MLOps Pipeline with MLflow, Kubeflow, and Airflow
IntermediateIn this task, students will build an end-to-end MLOps pipeline for a time series forecasting model using MLflow, Kubeflow, and Airflow.
Implement a Model Monitoring and Maintenance System
IntermediateIn this task, students will build a comprehensive model monitoring and maintenance system for a time series forecasting model deployed in production.
Prerequisites
- • Familiarity with machine learning concepts
- • Experience with Python programming
- • Basic understanding of time series analysis
Certificate
Certificate of Completion
Earn a certificate upon successful completion