Skip to content

Improving readability in intro. #1444

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Dec 16, 2024
Merged

Improving readability in intro. #1444

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@ layout: learningpathall
---

## Optimizing Performance
A first step towards achieving optimal performance in a ML Model is to identify what is consuming the most time and memory in your application. Profiling can help you identify the bottlenecks, and it can offer clues about how to optimize operations.
A first step towards achieving optimal performance in a Machine Learning Model is to identify what is consuming the most time and memory in your application. Profiling can help you identify the bottlenecks, and it can offer clues about how to optimize operations.

With Machine Learning (ML) applications, whilst the inference of the Neural Network (NN) is often the heaviest part of the application in terms of computation and memory usage, it is not necessarily always the case. It is therefore important to profile the application as a whole to detect other possible issues that can negatively impact performance, such as issues with pre- or post-processing, or the code itself.

Expand Down