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Serverless MLOps

In this example, we train and deploy a binary classification inference model using Scaleway Serverless Jobs and Container. To do this, we use the following resources:

  1. Serverless Job to populate data in S3
  2. Serverless Job for training
  3. Serverless Container for inference

We use object storage to share data between the steps.

Context

In this example we use a bank telemarketing dataset to predict if a client would engage in a term deposit subscription.

This dataset records marketing phone calls made to clients. The outcome of the phone call is in shown in the y column:

  • 0 : no subscription
  • 1 : subscription

Data Source

The dataset has many versions and is open-sourced and published here on the UCI Machine Leaning repository and is close to the one analyzed in the following research work:

  • [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Running the example

Step 0. Set up a Scaleway API key

For this example you will need to configure (or reuse) a Scaleway API key with permissions to create and update Serverless Containers and Jobs, as well as write to Object Storage buckets.

Step 1. Provision resources with Terraform

Set your Scaleway access key, secret key and project ID in environment variables:

export TF_VAR_access_key=<your-access-key>
export TF_VAR_secret_key=<your-secret-key>
export TF_VAR_project_id=<your-project-id> # you can create a separate project for this example

By default, both jobs and container trigger in the example run regularly on a schedule. The default values for these schedules are configured in jobs/ml-ops/terraform/variables.tf, and can be overridden using Terraform variables, e.g. export TF_VAR_data_fetch_cron_schedule="0 10 * * *".

Then deploy MLOps infrastructure using the following:

cd terraform
terraform init
terraform plan
terraform apply

Step 2. Optional: trigger jobs manually

The pipeline is automatic, all jobs will be run at their respective scheduled time. This step can be ignored unless for debugging or test purposes.

To run the jobs for the data and training, we can use the Scaleway CLI:

cd terraform
scw jobs run list project-id=<my_project_id>
scw jobs definition start $(terraform output -raw fetch_data_job_id | awk '{print substr($0, 8)}') project-id=<my_project_id>
scw jobs definition start $(terraform output -raw training_job_id | awk '{print substr($0, 8)}') project-id=<my_project_id>
scw jobs run list project-id=<my_project_id>

You can also trigger the jobs from the Jobs section of the Scaleway Console.

Step 3. Use the inference API

Load model with the latest version using:

cd terraform
export INFERENCE_URL=$(terraform output raw endpoint)
curl -X POST ${INFERENCE_URL}

Then post data to infer the class:

curl -X POST \
  -H "Content-Type: application/json" \
  -d @../inference/example.json
  ${INFERENCE_URL}/inference

Local testing

To test the example locally you can use Docker Compose.

# Build the containers locally
docker compose build

# Run the job to set up the data
docker compose up data

# Run the job to train and upload the model
docker compose up training

# Run the inference server
docker compose up inference

Access the inference API locally:

curl -X POST \
  -H "Content-Type: application/json" \
  -d @inference/example.json
  http://localhost:8080/inference