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Generative AI Ops: Scaling Diffusion Models in Production Virtual Internship

In this virtual internship, students will learn how to operationalize diffusion-based generative AI models, including model versioning, monitoring, and deployment strategies. They will gain hands-on experience with MLOps tools and techniques to scale these models in production environments. By the end of the internship, students will be equipped with the skills to effectively manage the lifecycle of diffusion models and deploy them at scale.

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

Diffusion Model Fundamentals

Advanced

In this task, students will explore the underlying principles of diffusion-based generative AI models, including their architecture, training, and sampling processes.

8 hours

MLOps for Generative AI

Advanced

In this task, students will explore the key MLOps practices and challenges for operationalizing diffusion-based generative AI models.

10 hours

Versioning Diffusion Models with MLflow

Advanced

In this task, students will implement MLflow to version and track their diffusion models.

12 hours

Advanced MLflow Features for Generative AI

Advanced

In this task, students will explore advanced MLflow features and how they can be leveraged to manage the lifecycle of diffusion-based generative AI models.

16 hours

Containerizing Diffusion Models with Docker

Advanced

In this task, students will learn how to containerize their diffusion models using Docker.

10 hours

Deploying Diffusion Models to Kubernetes

Advanced

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

16 hours

Monitoring Diffusion Models in Production

Advanced

In this task, students will implement a model monitoring pipeline for their deployed diffusion models.

14 hours

Maintaining Diffusion Models in Production

Advanced

In this task, students will learn how to maintain and update their deployed diffusion models to ensure reliable performance over time.

12 hours

Prerequisites

  • • Proficiency in Python
  • • Experience with machine learning and deep learning
  • • Familiarity with MLOps concepts

Certificate

Certificate of Completion

Earn a certificate upon successful completion