Mlops Intermediate Premium

Automated MLOps Pipelines with Airflow Virtual Internship

In this virtual internship, students will learn how to develop end-to-end machine learning workflows using Apache Airflow for orchestration, monitoring, and deployment. They will gain hands-on experience in building automated MLOps pipelines, leveraging tools like MLflow, Kubeflow, Docker, and Kubernetes. By the end of the internship, students will be able to create robust, scalable, and maintainable ML systems that can be easily deployed and monitored in production.

weeks
8 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

Set up an Airflow environment

Intermediate

In this task, students will set up a local Airflow environment and explore the basic components of an Airflow DAG.

4 hours

Explore Airflow's core concepts

Intermediate

In this task, students will dive deeper into Airflow's core concepts, such as operators, sensors, and task dependencies.

6 hours

Integrate MLflow with Airflow

Intermediate

In this task, students will learn how to use MLflow within Airflow to track experiments, log model artifacts, and manage the model registry.

8 hours

Integrate Kubeflow with Airflow

Intermediate

In this task, students will learn how to use Kubeflow components within Airflow to train and deploy machine learning models.

10 hours

Containerize an ML model using Docker

Intermediate

In this task, students will learn how to containerize an ML model using Docker, including creating a Dockerfile and building the Docker image.

6 hours

Deploy containerized models to Kubernetes

Intermediate

In this task, students will learn how to deploy their containerized ML models to a Kubernetes cluster.

8 hours

Implement model monitoring and alerting

Intermediate

In this task, students will learn how to set up model monitoring and alerting within their Airflow pipelines.

8 hours

Detect and remediate model drift

Intermediate

In this task, students will learn how to detect and remediate model drift within their Airflow-powered MLOps pipelines.

8 hours

Prerequisites

  • • Intermediate Python programming
  • • Basic understanding of machine learning concepts
  • • Familiarity with cloud computing and containers

Certificate

Certificate of Completion

Earn a certificate upon successful completion