fix(dswm): Add missing post

ds-with-mac/content/posts/testing-ml/index.md

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+---
+title: Testing of ML Systems
+seo_title: Testing of ML Systems
+summary: Testing of ML systems, what I wish I knew when I deployed my first ML system.
+description: 
+slug: testing-ml
+author: Marcus Elwin
+
+draft: false
+date: 2023-08-20T12:58:11+02:00
+lastmod: 
+expiryDate: 
+publishDate: 
+
+feature_image: 
+feature_image_alt: 
+
+categories:
+    - MLOps
+tags:
+    - Machine Learning
+    - Testing
+series:
+
+toc: true
+related: true
+social_share: true
+newsletter: true
+disable_comments: false
+---
+
+So you have secured data :tada: for your model and trained it with `model.train`, and maybe you have evaluate its performance on a hold-out test set or potential done an `A/B-test`. However, how do you know that your model will work, when deployed? 
+
+Can you ensure that your model still works after some slight changes in input data? In this article we will cover some considerations for how you can test your ML system to mitigate and take any relevant actions before and after you have deployed your model. 
+
+This post has been inspired by some previous work such as:
+* :computer: [How to Test Machine Learning Code and Systems](https://eugeneyan.com/writing/testing-ml/)
+* :computer: [Effective testing for machine learning systems](https://www.jeremyjordan.me/testing-ml/)
+
+## What is an ML system?
+__TODO__
+
+## Why should you think about tests for your ML system?
+__TODO__
+
+## Testing a ML system vs testing a traditional software system
+__TODO__
+
+## Pre-training test(s)
+__TODO__
+
+## Post-training test(s)
+__TODO__
+
+## Data-drift test(s)
+__TODO__
+
+