Easy Machine Learning Proof of Concepts Easy ML PoCs is a website/tool for use by people who need help navigating the AI/ML stack on AWS for their custom use cases. Customers who typically find it difficult to
-
The only thing you will have to touch, really, is the content folder. Add/ clone/ modify existing folders and .md markdown files.
-
To test locally, go to the root folder and do a 'hugo serve' and then head to localhost (link will be printed out in the output.
-
Commit and push once you are ready and the website will be built.
-
Do a PR for major changes, push directly for minor ones.
Then, create content pages inside the previously created chapter. Here are two ways to create content in a chapter with this hugo template:
hugo new chapter/first-content.md
hugo new chapter/second-content/_index.md
So how do you use this to contribute examples?
- Ask for access and clone repo
- Select between preprocessing, training and inference
- based on your selection above, create a new index file under this chapter and edit away. For example, to create a new preprocessing sub-chapter for personalize, do:
`hugo new preprocessing/personalize/_index.md'
- Push update and wait for build
Insert code samples in a `{{< highlight >}}' block as shown below:
`{{< highlight python >}} INSERT CODE HERE {{< /highlight >}}''
This gives you some more control like highlighting lines within an included code block.
Alternatively, use the simple git syntax
INSERT CODE HEREMIT-0
The "use cases" section uses the following chest xray dataset:
"Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, v2" used in examples under CC BY 4.0.
used in example uses cases and tutorials
On the page you edited, mark draft: false (will be true by default)