Recommendations Accelerator #5
Conversation
✅ Deploy Preview for data-product-accelerators ready!
To edit notification comments on pull requests, go to your Netlify site configuration. |
| 1h :s5, after s4, 05:00 | ||
| section 6. Next steps | ||
| 30min :s6, after s5, 05:30 | ||
| {{</mermaid>}} |
There was a problem hiding this comment.
unrelated to this PR, but Jack do you have any idea how to make this chart look better, so that the text doesn't go into the chart area? I tried when I was writing it but couldn't figure it out, and it looks kinda janky
|
Yes once this is merged it will appear at https://snowplow.io/data-product-accelerators/snowplow-recommendations/ It would definitely work to make branches off this PR and merge them as you go like a normal release though! |
… the Flask app; updated the create Personalize Python script.
content/snowplow-recommendations/supporting-infrastructure/build.md
Outdated
Show resolved
Hide resolved
content/snowplow-recommendations/supporting-infrastructure/build.md
Outdated
Show resolved
Hide resolved
…for the AWS Personalize role manual creation.
| In your AWS account, create an access key for your user - https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html#Using_CreateAccessKey. Copy the access key id and secret access key to a file called credentials in the .aws folder in your home directory. The file should look like this: | ||
| ``` |
There was a problem hiding this comment.
Could you just draw a file tree here to visulaise where this file is meant to go?
| In this accelerator, we will enable you to: | ||
|
|
||
| * Run dbt to transform your Snowplow data into a format that can be used by AWS Personalize | ||
| * Set up the infrastructure required to run AWS Personalize, including the necessary IAM roles and policies | ||
| * Create ecommerce and/or media (video on demand) recommenders in AWS Personalize | ||
| * Run a local Flask application to interact with AWS Personalize | ||
| *** |
There was a problem hiding this comment.
Can you add an image of the app here, having a picture on the front page is always good
|
|
||
| If you are not using the sample data: | ||
| - An implementation of the dbt-snowplow-ecommerce package. This will have created the `snowplow_ecommerce_product_interactions` table, which is used by this dbt package to create the ecommerce dataset for AWS Personalize. If you don't have this table, you can create it by following the instructions [here](https://docs.snowplow.io/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-quickstart/ecommerce/). | ||
|
|
||
| And/or | ||
|
|
||
| - An implementation of the dbt-snowplow-media-player package (>= 0.6.0). This will have created the `snowplow_media_player_base` table, which is used by this dbt package to create the video_on_demand dataset for AWS Personalize. If you don't have this table, you can create it by following the instructions [here](https://docs.snowplow.io/docs/modeling-your-data/modeling-your-data-with-dbt/dbt-quickstart/media-player/). |
There was a problem hiding this comment.
Is there a better way to format/write this?
There was a problem hiding this comment.
Add something here to note the dbt package is under SPAL licensing
| The dataset/group, schema and import job names can be configured to your preference. The `s3_bucket_name` should be just the name of the S3 bucket (not the full s3 path) you created either manually or via Terraform; similarly the `role_arn` is the name of the Personalize IAM role you created, it corresponds to the Terraform variable `personalize_role_name` with a default name of `PersonalizeIAMRole`. | ||
|
|
||
| You may choose which or both of the domains for Personalize to create by setting the `enabled` flag in the config file to `true` or `false`. Additionally you can choose which datasets to be created for training by Personalize by adding them to the list for the `datasets` parameter. Ensure that whichever datasets you add here are also exported by your dbt package by adding the corresponding table names to the `dbt_project.yml`'s `tables_to_export` config value. Finally you will be able to select which recommender you'd like Personalize to generate for your datasets by adding their config names in to the list parameter `recommenders` (refer to the table below for the list of available recommenders). |
|
|
||
| #### **Step 2:** Create virtual environment | ||
| Before running the script to create the recommenders with AWS personalize, use the requirements.txt file to set up the necessary packages in a virtual environment. | ||
|
|
There was a problem hiding this comment.
Specify python venv, maybe link to instructions to set up a venv?
|
|
||
| |  | | ||
| |:--:| |
There was a problem hiding this comment.
Can you make this not a full-width image? Seems a bit big
|
|
||
| Which recommenders are available to you will depend on the domain(s) you chose and the dataset types you created. See the below table for more information about each recommender: | ||
|
|
||
| | Domain | Recommender | Required dataset types | Optional dataset types | Required input parameters | Optional input parameters | |
There was a problem hiding this comment.
Maybe put this under a new header to make clearer it applies to all options?
There was a problem hiding this comment.
Don't we normally have a recommended set of other accelerators at the end or did I make that up?
Preview is found at https://deploy-preview-5--data-product-accelerators.netlify.app/accelerators/snowplow-recommendations/