Amazon SageMaker

Fairness and Model Explainability

Amazon SageMaker Clarify aids in the improvement of machine learning (ML) models by detecting potential bias and assisting in explaining the models' predictions. It aids in detecting various types of bias in pretraining and posttraining data that can arise during model training or production. SageMaker Clarify explains how these models use a feature attribution approach to make predictions. It also checks for bias or feature attribution drift in inferences made by models in production. SageMaker Clarify's fairness and explainability functionality include components that assist AWS customers in creating less biassed and more understandable machine learning models. It also contains tools to help you in creating model governance reports for use by risk and compliance teams and external regulators.

Machine learning models and data-driven systems are increasingly being used to aid decision-making in various industries, including finance, healthcare, education, and human resources. Machine learning applications help meet regulatory requirements, improve business decisions, and provide better insights into data science procedures by improving accuracy, productivity, and cost savings.

📔Data Science: Ml engineers and data scientists need to update the tools according to model requirements regularly. They have to keep an eye on all the model that provides disturbances and other hindrances, and after engineering, they have to remove that.
 

📔Regulatory: We have to understand why an ML model had made a specific prediction and whether it was influenced by any method during training or not. There are policy and ethical challenges posed by ML data-driven systems.
 

📔Business: Adopting AI systems in regulated domains necessitates trust, which can be established by providing credible explanations of trained model behaviour and how deployed models make predictions. Financial services, human resources, healthcare, and automated transportation are examples of industries where model explainability is critical.

Practices for Evaluating Fairness and Explainability in ML Life Cycle

You can follow certain practices to evaluate fairness and explainability in the ML life cycle.

1️⃣Explainability and Fairness by Design: You have to consider explainability and fairness in each stage of the ML cycle: dataset construction, problem formation, model training process, algorithm selection, deployment, and feedback.
 

2️⃣Fairness as a process: Fairness is highly dependent on the application. Furthermore, social, legal and other non-technical considerations may need to guide the selection of the attributes for which bias is to be measured and the choice of bias metrics. Building consensus and collaboration among key stakeholders (including product, policy, legal, engineering, AI/ML teams, and end-users and communities) is essential for successfully implementing fairness-aware ML approaches.

Working with Models

There are many steps involved while working with models. We will discuss each step in detail while moving forward in this section.

Train a Model

To train a model in sagemaker, you need to create a training job. Your training job must include the following instructions:

📙The URL of Amazon S3 where the training data has been stored.
 

📙The URL of the Amazon S3 bucket is where you want to keep the output.
 

📙All the resources required by sagemaker for model training.
 

You have some options available for training algorithm:

📗Use sagemaker provided algorithm: Sagemaker provides many algorithms. If any one of them satisfies your requirements, you can use it.
 

📗Use Sagemaker Debugger: To check the data and training parameters while working or learning different frameworks sagemaker debugger helps you detect errors automatically.

Deploy a Model

You can use Amazon SageMaker to deploy the project after training it to get predictions:

📘You can use SageMaker's real-time service to make persistent predictions.
 

📘Can use serverless inference for idle workloads for a particular time.
 

📘Asynchronous abstraction can be used for large payloads with low latency requirements.

Features

SageMaker provides many features to manage and optimize resources.

📔It can manage models on edge devices to secure, maintain ML models, monitor, and optimize a fleet of edge devices like robots, smart cameras, mobile devices, and personal computers.
 

📔To optimize Py Torch, Tensor Flow-Lite, Tensor Flow, Keras, Gluon, and ONMX models for inference on windows, Linux, and various windows based processors Intel, ARM, Qualcomm, Nvidia, etc.

Validating an ML model

After training a model, we have to evaluate it in some scenarios to test its accuracy and performance are aligned with our business goals or not. We have various methods to validate the model:

📙Testing with Live Data: It supports testing by using different production variants. You can understand production variants as the models deployed on the same sagemaker and use the same inference code. We give a small fraction of total traffic for validation.
 

📙Offline testing: Here, we will not use live data. We will use the historical data. Deploy the model at the alpha endpoint. Here we will use a jupyter notebook to send data.
 

📙Validation using a holdout set: Ml practitioners set aside some part of the data as a holdout set. With the help of this, you can check how much inference your model is providing on the holdout set. This approach will also give you an idea of how often you will get the correct answers.
 

📙K-fold validation: In this, you distribute or split the dataset into k parts and use one of the k parts as a holdout.

Model Monitoring

Use the Amazon SageMaker model monitor to continuously monitor the quality of your machine learning models in real-time after you deploy a model into your production environment. When there are deviations in the model quality, such as data drift and anomalies, the Amazon SageMaker model monitor allows you to set up an automated alert triggering system. Amazon CloudWatch Logs collects log files of model status monitoring and notifies you when the model's quality falls below certain thresholds you set. CloudWatch saves the log files to an Amazon S3 bucket that you specify. Model deviations can be detected early and pro-actively using AWS model monitor products, allowing you to take immediate action to maintain and improve the quality of your deployed model.

Frequently Asked Questions

How does Amazon SageMaker secure the code?

It stores the code in ML storage volumes and with proper encryption.

Can we use your tools with Amazon SageMaker?

Although Amazon SageMaker provides complete end-to-end encryption, you can still use your tools with it without any problem.

Write some of the primary advantages of Amazon SageMaker.

With the help of this, you can deploy an inference pipeline, preprocess the input and work on it.

What is SageMaker SDK?

It is an open-source library for deploying and training ML models on Amazon SageMaker.

Conclusion

In this article, we discussed Amazon SageMakaer and its features, Machine learning, fairness and model explainability. We have also discussed the model in detail with an appropriate explanation of each step, like the training, deployment, validation, and monitoring of the model.

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