SRE Strategies for Machine Learning Platforms Virtual Internship
In this virtual internship, students will explore the best practices and strategies for Site Reliability Engineering (SRE) in the context of machine learning platforms and pipelines. They will learn how to design, deploy, and manage reliable, scalable, and observable ML systems, focusing on areas such as infrastructure automation, monitoring, incident management, and continuous improvement.
Track Overview
Tasks & Milestones
Task 1: Analyzing Reliability Challenges in ML Systems
AdvancedIn this task, students will analyze common reliability challenges faced by machine learning platforms and identify SRE strategies to address them.
Task 1: Implementing a Kubernetes-based ML Pipeline
AdvancedIn this task, students will design and implement a Kubernetes-based deployment for a machine learning pipeline, including automation of infrastructure components.
Task 1: Implementing a Monitoring and Observability Stack for an ML Platform
AdvancedIn this task, students will design and implement a monitoring and observability solution for a machine learning platform, including the definition of SLIs and SLOs.
Task 1: Implementing an Incident Management Process for an ML Platform
AdvancedIn this task, students will design and implement an incident management process for a machine learning platform, including the development of response and escalation procedures.
Prerequisites
- • Proficiency in a programming language (e.g., Python, Go)
- • Experience with cloud infrastructure and containerization (e.g., Kubernetes)
- • Familiarity with machine learning concepts and workflows
Certificate
Certificate of Completion
Earn a certificate upon successful completion