Mlops Intermediate Premium

Scalable MLOps with Kubernetes Virtual Internship

In this virtual internship, students will learn how to build and deploy machine learning models at scale using Kubernetes and cloud-native technologies. They will gain hands-on experience with tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes, and learn best practices for model monitoring and deployment.

weeks
4 tasks
0 enrolled
Sign In to Purchase - $49
Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Containerize a Machine Learning Model

Intermediate

In this task, students will learn how to containerize a machine learning model using Docker.

8 hours

Develop a Kubeflow Pipeline

Intermediate

In this task, students will learn how to create and deploy a Kubeflow pipeline for a machine learning workflow.

12 hours

Implement Model Monitoring with MLflow

Intermediate

In this task, students will learn how to use MLflow to monitor the performance of a deployed machine learning model.

10 hours

Develop an Airflow DAG for an ML Workflow

Intermediate

In this task, students will learn how to create and deploy an Airflow DAG for a machine learning workflow.

12 hours

Prerequisites

  • • Intermediate knowledge of Python
  • • Experience with machine learning and data science
  • • Basic understanding of Docker and Kubernetes

Certificate

Certificate of Completion

Earn a certificate upon successful completion