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Zoumana Keita 7c8ee0f56d
Update README.md
1 year ago
ee9d73ee3c
Adding the configuration file
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
579984ea00
Adding the general workflow
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
7c8ee0f56d
Update README.md
1 year ago
5b0c4743fc
Update config.yaml
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
a287d902e8
model and data folders
1 year ago
Storage Buckets
Data Pipeline
Legend
DVC Managed File
Git Managed File
Metric
Stage File
External File

README.md

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How to Use an AWS EC2 instance as Self-hosted Runner for Continuous Machine Learning.

DagsHub + Actions + EC2 + CML

After completing this repository, you will be able to understand the following concepts:

  • Provision an AWS EC2 and running the model training with CML
  • Implement a Github actions pipeline using the previous instance.
  • Automatically log your models metrics with MLFlow.
  • Automatically save your training metadata on DVC for easy tracking.

MLOps Workflow

Tip!

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