Creating a batch inference job (console) - Amazon Personalize
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Creating a batch inference job (console)

After you have completed Preparing input data for batch recommendations, you are ready to create a batch inference job. This procedure assumes that you have already created a solution and a solution version (trained model).

To create a batch inference job (console)

  1. Open the Amazon Personalize console at https://console.amazonaws.cn/personalize/home and sign in to your account.

  2. On the Dataset groups page, choose your dataset group.

  3. From the navigation pane, under Custom resources, choose Batch inference jobs.

  4. Choose Create batch inference job.

  5. Choose the batch inference job type.

    • To generate item recommendations without themes, choose Item recommendations.

    • If you use the Similar-Items recipe and want to add descriptive themes to groups of similar items, choose Themed recommendations with Content Generator. To generate themes, you must have an Items dataset with item name data and textual data. For more information, see Batch recommendations with themes from Content Generator.

  6. In Batch inference job details, in Batch inference job name, specify a name for your batch inference job.

  7. For Solution, choose the solution and then choose the Solution version ID that you want to use to generate the recommendations.

  8. For Number of results, optionally specify the number of recommendations for each line of input data. The default is 25.

  9. If your batch job generates recommendations with themes, in Themed recommendations details, choose the column containing names or titles for the items in your Items dataset. This data can help generate more relevant themes. For more information, see Batch recommendations with themes from Content Generator.

  10. In Input source, specify the Amazon S3 path to your input file.

    Use the following syntax: s3://<name of your S3 bucket>/<folder name>/<input JSON file name>.json

    Your input data must be in the correct format for the recipe your solution uses. For input data examples see Batch inference job input and output JSON examples.

  11. For Decryption key, if you use your own Amazon KMS key for bucket encryption, specify the Amazon Resource Name (ARN) of your key. Amazon Personalize must have permission to use your key. For information about granting permissions, see Giving Amazon Personalize permission to use your Amazon KMS key.

  12. In Output destination, specify the path to your output location. We recommend using a different location for your output data (either a folder or a different Amazon S3 bucket).

    Use the following syntax: s3://<name of your S3 bucket>/<output folder name>/

  13. For Encryption key, if you use your own Amazon KMS key for encryption, specify the ARN of your key. Amazon Personalize must have permission to use your key. For information about granting permissions, see Giving Amazon Personalize permission to use your Amazon KMS key.

  14. For IAM service role, choose the IAM service role you created for Amazon Personalize during set up. This role must have read and write access to your input and output Amazon S3 buckets respectively.

  15. In Filters optionally choose a filter to apply a filter to the batch recommendations. If your filter uses placeholder parameters, make sure the values for the parameters are included in your input JSON. For more information see Providing filter values in your input JSON.

  16. For Tags, optionally add any tags. For more information about tagging Amazon Personalize resources, see Tagging Amazon Personalize resources.

  17. Choose Create batch inference job. Batch inference job creation starts and the Batch inference jobs page appears with the Batch inference job detail section displayed.

    When the batch inference job's status changes to Active, you can retrieve the job's output from the designated output Amazon S3 bucket. The output file's name will be of the format input-name.out.