Merge pull request #93 from zishenwan/fig_add

Adding fig to embedded_ml cloud ml -- thank you @zishenwan 🙏
harvard-edge · Dec 5, 2023 · 0bd1e48 · 0bd1e48
2 parents 1b4c78e + 8b51765
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diff --git a/embedded_ml.qmd b/embedded_ml.qmd
@@ -42,6 +42,8 @@ At its foundation, Cloud ML utilizes a powerful blend of high-capacity servers,
 
 The cloud environment excels in data processing and model training, designed to manage large data volumes and complex computations. Models crafted in Cloud ML can leverage vast amounts of data, processed and analyzed centrally, thereby enhancing the model's learning and predictive performance.
 
+![Cloud ML Example: Google Tensor Pods (Source: [InfoWorld](https://www.infoworld.com/article/3197331/googles-new-tpus-are-here-to-accelerate-ai-training.html))](images/imgs_embedded_ml/cloud_ml_tpu.jpg)
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 ### Benefits
 
 Cloud ML is synonymous with immense computational power, adept at handling complex algorithms and large datasets. This is particularly advantageous for machine learning models that demand significant computational resources, effectively circumventing the constraints of local setups.

diff --git a/images/imgs_embedded_ml/cloud_ml_tpu.jpg b/images/imgs_embedded_ml/cloud_ml_tpu.jpg