Skip to main content
If you prefer to use Terraform, jump to Applying Bucket Policy & Tag via Terraform
Set up an import job to log inference files to Arize. Updates to files are checked every 10 seconds. Users generally find a sweet spot around a few hundred thousand to a million rows in each file with the total file limit being 1GB.

​
Select Amazon S3

Navigate to the ‘Upload Data’ page on the left navigation bar in the Arize platform. From there, select the ‘AWS S3’ card to begin a new file import job.

Step 1: Select Amazon S3

​
Enable Access To Individual or Multiple Buckets

There are two ways to setup access permissions with Arize

Configure An Individual Bucket Policy

Give Arize permission to access individual buckets

Configure Multiple Buckets Via Role Based Permissions

Assign Arize a role to access multiple buckets using external IDs

​
Add File Path

Fill in the file path where you would like Arize to pull your model’s inferences. Arize will automatically infer your bucket name and prefix.

Example File Path In Arize UI

Create the path to your bucket and folder to pull your model’s inferences. In this example, you might have an AWS bucket and folder named s3://example-demo-bucket/click-thru-rate/production/v1/ that contains parquet files of your model inferences. Your bucket name is example-demo-bucket and your prefix is click-thru-rate/production/v1/.
The file structure can take into consideration various model environments (training, production, etc) and locations of ground truth. In addition, S3 bucket import allows recursive operations. This means that it will include all nested subdirectories within the specified bucket prefix, regardless of the number or depth of these directories.
File Directory ExampleThere are multiple ways to structure your file directory. If actuals and predictions can be sent together, simply store this data in a the same file and import this data together through a single file importer job.In the case of delayed actuals, we recommend you separate your predictions and actuals into separate folders and loading this data through two separate file importer jobs. Learn more here.
s3://bucket1/click-thru-rate/production/prediction/
├── 11-19-2022.parquet 
├── 11-20-2022.parquet
├── 11-21-2022.parquet

s3://bucket1/click-thru-rate/production/actuals/
├── 12-1-2022.parquet # same prediction id column, model, and space as the corresponding prediction
├── 12-2-2022.parquet
└── 12-3-2022.parquet

​
Configure Ingestion Key

Tag your bucket with the key arize-ingestion-key and the provided tag value (i.e. AWS Object Tags).In Arize UI: Copy arize-ingestion-key value

Example Bucket Tag In Arize UI

In AWS Console: Navigate to your S3 bucket -> Properties -> Edit Bucket Policy

Navigate to your bucket properties tab

In AWS Console: Set tag Key = arize-ingestion-key and Value as the value copied from Arize UI from the previous step

Add arize-ingestion-key as a bucket tag

​
Enable Bucket Policy Permissions

In Arize UI: Copy the policy supplied by Arize in the file importer job setup

Copy AWS Policy In Arize UI

In the AWS console: Navigate to your S3 bucket -> Permission -> Edit Bucket Policy

Add/Edit bucket policy

In the AWS console: Paste the above AWS policy from Arize UI into the bucket policy

Add policy to your bucket

​
Define Your Model Schema

Model schema parameters are a way of organizing model inference data to ingest to Arize. When configuring your schema, be sure to match your data column headers with the model schema. You can either use a form or a simple JSON-based schema to specify the column mapping. Arize supports CSV, Parquet, Avro, and Apache Arrow. Refer here for a list of the expected data types by input type.

File Schema Form Inputs

Form Schema JSON Inputs

Learn more about Schema fields here.

​
Validate Your Model Schema

Once you fill in your applicable predictions, actuals, and model inputs, click ‘Validate Schema’ to visualize your model schema in the Arize UI. Check that your column names and corresponding data match for a successful import job.
Once finished, your import job will be created and will start polling your bucket for files.
If your model receives delayed actuals, connect your predictions and actuals using the same prediction ID, which links your data together in the Arize platform. Arize regularly checks your data source for both predictions and actuals, and ingests them separately as they become available. Learn more here.

​
Check Job Status

Arize will attempt a dry run to validate your job for any access, schema, or record-level errors. If the dry run is successful, you can proceed to create the import job. From there, you will be taken to the ‘Job Status’ tab. All active jobs will regularly sync new data from your data source with Arize. You can view the job details by clicking on the job ID, which reveals more information about the job.

Job status page showing job listings

To pause, delete, or edit your file schema, click on ‘Job Options’.
  • Delete a job if it is no longer needed or if you made an error connecting to the wrong bucket. This will set your job status as ‘deleted’ in Arize.
  • Pause a job if you have a set cadence to update your table. This way, you can ‘start job’ when you know there will be new data to reduce query costs. This will set your job status as ‘inactive’ in Arize.
  • Edit a file schema if you have added, renamed, or missed a column in the original schema declaration.

​
Troubleshoot Import Job

An import job may run into a few problems. Use the dry run and job details UI to troubleshoot and quickly resolve data ingestion issues.

​
Validation Errors

If there is an error validating a file against the model schema, Arize will surface an actionable error message. From there, click on the ‘Fix Schema’ button to adjust your model schema.

​
Dry Run File/Table Passes But The Job Fails

If your dry run is successful, but your job fails, click on the job ID to view the job details. This uncovers job details such as information about the file path or query id, the last import job, potential errors, and error locations.
Once you’ve identified the job failure point, fix the file errors and reupload the file to Arize with a new name.

​
Applying Bucket Policy & Tag via Terraform

resource "aws_s3_bucket" "arize-example-bucket" {
  bucket = "my-arize-example-bucket"
  tags = {
    arize-ingestion-key = "value_from_arize_ui"
  }
}

resource "aws_s3_bucket_policy" "grant_arize_read_only_access" {
  bucket = aws_s3_bucket.arize-example-bucket.id
  policy = data.aws_iam_policy_document.grant_arize_read_only_access.json
}

data "aws_iam_policy_document" "grant_arize_read_only_access" {
  statement {
    principals {
      type        = "AWS"
      identifiers = ["arn:aws:iam::<REDACTED>:role/arize-importer"]
    }

    actions = [
      "s3:GetBucketTagging",
      "s3:GetObject",
      "s3:ListBucket",
    ]

    resources = [
      aws_s3_bucket.arize-example-bucket.arn,
      "${aws_s3_bucket.arize-example-bucket.arn}/*",
    ]
  }
}