Sre Intermediate Premium

SRE for Machine Learning and Data Pipelines Virtual Internship

In this virtual internship, students will learn how to ensure the reliability and availability of machine learning models and data pipelines. They will gain hands-on experience with model versioning, data quality monitoring, and incident response. By the end of the internship, students will be equipped with the skills to become Site Reliability Engineers (SREs) for machine learning and data-intensive applications.

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

Task 1: Implement Model Versioning

Intermediate

In this task, students will set up a model versioning system using Git and MLflow to track model changes and artifacts.

10 hours

Task 2: Develop Automated Model Deployment Pipeline

Intermediate

In this task, students will create an automated pipeline to deploy machine learning models to production.

15 hours

Task 1: Implement Data Quality Monitoring

Intermediate

In this task, students will set up data quality checks and monitoring for their machine learning pipelines.

12 hours

Task 2: Set up Observability for Machine Learning Pipelines

Intermediate

In this task, students will implement observability tools to monitor the health and performance of their machine learning pipelines.

15 hours

Task 1: Develop Incident Response and Mitigation Plan

Intermediate

In this task, students will create an incident response and mitigation plan for a machine learning pipeline.

12 hours

Task 2: Implement SLIs and SLOs for Reliability Engineering

Intermediate

In this task, students will define service-level indicators (SLIs) and objectives (SLOs) to measure and improve the reliability of a machine learning pipeline.

15 hours

Task 1: Implement Infrastructure as Code

Intermediate

In this task, students will use Terraform to define and manage the infrastructure for a machine learning pipeline.

15 hours

Task 2: Implement Workflow Automation

Intermediate

In this task, students will use Airflow to automate the workflows and processes for a machine learning pipeline.

15 hours

Prerequisites

  • • Familiarity with cloud computing and containerization
  • • Experience with Python or other programming languages

Certificate

Certificate of Completion

Earn a certificate upon successful completion