Merge pull request #616 from harvard-edge/611-update-ai-frameworks-ch…

…apter

611 Update AI Frameworks chapter

contents/core/benchmarking/benchmarking.qmd

@@ -264,7 +264,7 @@ The development life cycle of a machine learning model involves two critical pha
 
 From a systems perspective, training machine learning models is resource-intensive, especially when working with large models. These models often contain billions or even trillions of trainable parameters and require enormous amounts of data, often on the scale of many terabytes. For example, [OpenAI's GPT-3](https://arxiv.org/abs/2005.14165) [@brown2020language] has 175 billion parameters, was trained on 45 TB of compressed plaintext data, and required 3,640 petaflop-days of compute for pretraining. ML training benchmarks evaluate the systems and resources required to manage the computational load of training such models.
 
-Efficient data storage and delivery during training also play a major role in the training process. For instance, in a machine learning model that predicts bounding boxes around objects in an image, thousands of images may be required. However, loading an entire image dataset into memory is typically infeasible, so practitioners rely on data loaders (as disucssed in @sec-frameworks-data-loaders) from ML frameworks. Successful model training depends on timely and efficient data delivery, making it essential to benchmark tools like data loaders, data pipelines, preprocessing speed, and storage retrieval times to understand their impact on training performance.
+Efficient data storage and delivery during training also play a major role in the training process. For instance, in a machine learning model that predicts bounding boxes around objects in an image, thousands of images may be required. However, loading an entire image dataset into memory is typically infeasible, so practitioners rely on data loaders from ML frameworks. Successful model training depends on timely and efficient data delivery, making it essential to benchmark tools like data loaders, data pipelines, preprocessing speed, and storage retrieval times to understand their impact on training performance.
 
 Hardware selection is another key factor in training machine learning systems, as it can significantly impact training time. Training benchmarks evaluate CPU, GPU, memory, and network utilization during the training phase to guide system optimizations. Understanding how resources are used is essential: Are GPUs being fully leveraged? Is there unnecessary memory overhead? Benchmarks can uncover bottlenecks or inefficiencies in resource utilization, leading to cost savings and performance improvements.