Mlops Intermediate Premium

Edge MLOps: Deploying Models to IoT Devices Virtual Internship

In this virtual internship, students will learn how to optimize machine learning models for deployment on edge devices and manage the complete lifecycle using containerization and orchestration. They will gain hands-on experience with tools like MLflow, Kubeflow, Airflow, Docker, and Kubernetes to build and deploy ML models to IoT devices, ensuring efficient and reliable model management and monitoring.

weeks
8 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

Task 1: Model Optimization for Edge Deployment

Intermediate

In this task, students will optimize a pre-trained machine learning model for deployment on an edge device, focusing on techniques to reduce the model size and improve inference performance.

10 hours

Task 2: Edge-Optimized Model Deployment

Intermediate

In this task, students will deploy the optimized machine learning model to an edge device and test its performance in a real-world scenario.

12 hours

Task 1: Containerizing an ML Pipeline

Intermediate

In this task, students will containerize a complete machine learning pipeline using Docker, including model training, evaluation, and serving components.

12 hours

Task 2: Orchestrating ML Pipelines with Kubernetes

Intermediate

In this task, students will deploy the containerized ML pipeline to a Kubernetes cluster, leveraging the platform's capabilities for scalable and reliable model management.

15 hours

Task 1: Model Versioning and Tracking with MLflow

Intermediate

In this task, students will use MLflow to version and track their machine learning models, enabling better model management and reproducibility.

8 hours

Task 2: Automating Model Deployment with CI/CD

Intermediate

In this task, students will set up a Continuous Integration and Continuous Deployment (CI/CD) pipeline to automate the deployment of their machine learning models.

12 hours

Task 1: Implementing Model Monitoring and Drift Detection

Intermediate

In this task, students will set up a model monitoring solution to track the performance and detect drift in their machine learning models deployed on edge devices.

12 hours

Task 2: Developing an End-to-End MLOps Pipeline

Intermediate

In this final task, students will integrate all the concepts and tools learned throughout the virtual internship to develop a comprehensive end-to-end MLOps pipeline for their machine learning models.

20 hours

Prerequisites

  • • Intermediate Python programming
  • • Basic understanding of machine learning concepts
  • • Familiarity with Docker and Kubernetes

Certificate

Certificate of Completion

Earn a certificate upon successful completion