Mlops Intermediate Premium

MLOps for NLP: Transformers and MLflow Virtual Internship

In this virtual internship, students will learn how to operationalize natural language processing (NLP) models using MLflow for experiment tracking, model registry, and deployment. They will gain hands-on experience with MLOps practices, including model versioning, packaging, and serving. By the end of the internship, students will be able to build and deploy production-ready NLP models using Transformers and MLflow.

weeks
7 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 MLOps Concepts

Intermediate

In this task, students will research and summarize the key principles of MLOps, including model versioning, experiment tracking, and model deployment.

8 hours

Set up MLflow for NLP Experiments

Intermediate

In this task, students will set up an MLflow tracking server and configure it to track experiments for a Transformer-based NLP model.

12 hours

Analyze NLP Experiment Runs with MLflow

Intermediate

In this task, students will use MLflow to compare and analyze different experiment runs for their Transformer-based NLP model.

8 hours

Package NLP Models with MLflow

Intermediate

In this task, students will package their Transformer-based NLP models using MLflow's model format and register them in the MLflow Model Registry.

10 hours

Deploy NLP Models using Docker and Kubernetes

Intermediate

In this task, students will deploy their Transformer-based NLP models using Docker and Kubernetes, leveraging MLflow's deployment capabilities.

16 hours

Implement Model Monitoring for NLP Models

Intermediate

In this task, students will set up model monitoring for their Transformer-based NLP models, including metrics tracking and drift detection.

12 hours

Automate NLP Model Retraining and Deployment

Intermediate

In this task, students will implement a workflow to automatically retrain and redeploy their Transformer-based NLP models based on monitoring data.

16 hours

Prerequisites

  • • Familiarity with Python and machine learning
  • • Basic understanding of natural language processing
  • • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow)

Certificate

Certificate of Completion

Earn a certificate upon successful completion