Mlops Intermediate Premium

Serverless MLOps with AWS Virtual Internship

In this virtual internship, students will learn how to build a scalable, serverless MLOps pipeline using AWS services like Lambda, SageMaker, and CloudWatch. They will gain hands-on experience in automating the machine learning lifecycle, from data preprocessing to model deployment and monitoring. By the end of the internship, students will be equipped with the skills to build and manage robust, cloud-native ML systems.

weeks
4 tasks
0 enrolled
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Track price: $49.00

Track Overview

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

Tasks & Milestones

Explore Serverless MLOps Concepts

Intermediate

In this task, students will research and discuss the key principles and benefits of serverless MLOps, as well as the challenges and considerations involved in its implementation.

4 hours

Implement a Serverless Data Ingestion Pipeline

Intermediate

In this task, students will build a serverless data ingestion pipeline using AWS Lambda and S3 to automatically process and store incoming data.

8 hours

Build a Serverless Model Training Pipeline

Intermediate

In this task, students will create a serverless model training pipeline using AWS SageMaker and integrate it with the data pipeline from the previous module.

12 hours

Implement Model Monitoring and Alerting

Intermediate

In this task, students will set up model monitoring and alerting using AWS CloudWatch, integrating it with the previous modules to create a complete MLOps pipeline.

8 hours

Prerequisites

  • • Basic understanding of machine learning concepts
  • • Familiarity with Python programming
  • • Experience with AWS services (recommended)

Certificate

Certificate of Completion

Earn a certificate upon successful completion