## 2 Understanding the parameters

Because model performance can change over inference results, you need to implement an automated ML workflow that can continuously retrain a model with newly captured and human-labeled images. In addition, you need to consider adding flexibility into the ML workflow to allow for change without requiring development rework as business objectives evolve over time. Developing a customizable ML workflow that behaves similar to a business rule engine requires significant upfront investment, which can be a resource challenge.

In this demo, we provide a set of configurable parameters for you to use to customize the ML workflow. Go to [AWS Systems Manager Parameter Store](https://console.aws.amazon.com/systems-manager/parameters). You should see a list of 11 parameters as shown below:

- **Automatic-Human-Sampling-Frequency** - This is the polling frequency (in integer minutes) on how often the [Amazon EventBridge](https://aws.amazon.com/eventbridge/) Schedule rule is triggered to initiate the check for human sampling. As you update this value, the value is applied to the rule by an [AWS Lambda](https://aws.amazon.com/lambda) function.
- **Automatic-Training-Poll-Frequency** - This is the polling frequency (in integer minutes) on how often the [Amazon EventBridge](https://aws.amazon.com/eventbridge/) Schedule rule is triggered to initiate the check for automatic model training. As you update this value, the value is applied to the rule by an [AWS Lambda](https://aws.amazon.com/lambda) function.
- **Enable-A2I-Workflow** - This value (`true/false`) determines whether an [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/a2i-start-human-loop.html) Human Loop is created when the inference confidence level from the [Amazon Rekognition Custom Labels](https://aws.amazon.com/rekognition/custom-labels-features/) detection result is less than the [minimum confidence](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/detecting-custom-labels.html) as set in the **Minimum-Label-Detection-Confidence** parameter.
- **Enable-Automatic-Human-Sampling** - This value (`true/false`) determines whether automatic human sampling is `enabled/disabled`. When this value is updated to `true`, a Lambda function enables the EventBride Schedule rule. When the value is updated to `false`, a Lambda function disables the EventBride Schedule rule.
- **Enable-Automatic-Training** - This value (`true/false`) determines whether automatic model training is `enabled/disabled`. When this value is updated to `true`, a Lambda function enables the EventBride Schedule rule. When the value is updated to `false`, a Lambda function disables the EventBride Schedule rule.
- **For-System-Use-Only** - Do **NOT** modify this value. This is a reserved parameter consisting of environmental variables and operation data. The value is used and updated by Lambda functions.
- **Human-Sampling-Interval** - This value (Int) represents the minimum number of new Rekognition Custom Labelsdetections since the last human sampled detection before a new human sample is needed. If a new detection meets the interval requirement, it is marked for human sampling. An Amazon A2I human workflow is created, if a human workflow does not exist already.
- **Minimum-F1-Score** - This value is the minimum [F1 score](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/gs-step-evaluate-model.html) (Float 0.00-1.00) that determines whether a newly trained model is acceptable for deployment. If the **F1 score** of the trained Model is greater or equal to the **Minimum-F1-Score** value, the model will be deployed. If not, the model is marked as failed training.
- **Minimum-Inference-Units** - This value (Int), with a minimum of `1`, is number of [inference unit](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/rm-run-model.html) to use for the running Rekognition Customs Labelsmodel. A single inference unit represents 1 hour of processing and can support up to 5 Transaction Pers Second (TPS). Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.
- **Minimum-Label-Detection-Confidence** - Rekognition Custom Labels returns a **Confidence** between 0-100 on each detection. The **Minimum Confidence** (Float 0.00-100.00) determines whether a detection result is acceptable. If the detection **Confidence** is greater than or equal to the **Minimum Confidence**, then the detection is accepted. If not, the image is sent to [Amazon Augmented AI](https://aws.amazon.com/augmented-ai/) (Amazon A2I) human labeling process, provided that **Enable-A2I-Workflow** is `enabled`.
- **Minimum-Untrained-Images** - This value (Int) represents the minimum number of untrained or newly added images that will qualify the state machine to start a new model training process. At the instance the polled event is triggered, a Lambda function first determines the total number of training images in the designated S3 folder. Next it retrieves the value `previous_trained_images` from the parameter **For-System-Use-Only**. If the difference between the total number of images in S3 and the previous_trained_images is greater than or equal to minimum untrained images, then it will trigger a new model training. On a successful training, a Lambda function updates the `previous_trained_images` with the current total number of images trained.


**Note:** The Parameter store uses string to store value regardless of type. Do **NOT** use comma separator for numeric values.

Next Step: [3-Model-Training](../3-Model-Training/)