As machine learning moves from the research lab to the enterprise, questions around ethics and risk become even more critical for producing well-performing models. Bias goes against a code of ethics and can present itself throughout the machine learning lifecycle. We will explore how bias can appear in your people, strategy, data, algorithms, and models. I’ll share questions to ask during each stage to help root out bias. We will explore Amazon SageMaker Clarify, which detects potential bias throughout the ML lifecycle and helps to add a level of explainability to predictions.
Learning Objectives:
* Objective 1: Understand how bias can present itself across the ML lifecycle.
* Objective 2: Learn techniques on how to root out bias throughout the ML lifecycle.
* Objective 3: Understand how Amazon SageMaker Clarify can detect potential bias during data preparation, after model training, and in deployed models and help explain how input features contribute to model predictions in real time.
***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/sagemaker/clarify/?sagemaker-data-wrangler-whats-new.sort-by=item.additionalFields.postDateTime&sagemaker-data-wrangler-whats-new.sort-order=desc
****To download a copy of the slide deck from this webinar visit: https://pages.awscloud.com/AWS-ML-Heroes-in-15-Detect-Bias-in-ML-Data-Models-and-Explain-Predictions_2023_SN-0504-MCL_OD Subscribe to AWS Online Tech Talks On AWS:
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☁️ AWS Online Tech Talks cover a wide range of topics and expertise levels through technical deep dives, demos, customer examples, and live Q&A with AWS experts. Builders can choose from bite-sized 15-minute sessions, insightful fireside chats, immersive virtual workshops, interactive office hours, or watch on-demand tech talks at your own pace. Join us to fuel your learning journey with AWS.
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Learning Objectives:
* Objective 1: Understand how bias can present itself across the ML lifecycle.
* Objective 2: Learn techniques on how to root out bias throughout the ML lifecycle.
* Objective 3: Understand how Amazon SageMaker Clarify can detect potential bias during data preparation, after model training, and in deployed models and help explain how input features contribute to model predictions in real time.
***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/sagemaker/clarify/?sagemaker-data-wrangler-whats-new.sort-by=item.additionalFields.postDateTime&sagemaker-data-wrangler-whats-new.sort-order=desc
****To download a copy of the slide deck from this webinar visit: https://pages.awscloud.com/AWS-ML-Heroes-in-15-Detect-Bias-in-ML-Data-Models-and-Explain-Predictions_2023_SN-0504-MCL_OD Subscribe to AWS Online Tech Talks On AWS:
https://www.youtube.com/@AWSOnlineTechTalks?sub_confirmation=1
Follow Amazon Web Services:
Official Website: https://aws.amazon.com/what-is-aws
Twitch: https://twitch.tv/aws
Twitter: https://twitter.com/awsdevelopers
Facebook: https://facebook.com/amazonwebservices
Instagram: https://instagram.com/amazonwebservices
☁️ AWS Online Tech Talks cover a wide range of topics and expertise levels through technical deep dives, demos, customer examples, and live Q&A with AWS experts. Builders can choose from bite-sized 15-minute sessions, insightful fireside chats, immersive virtual workshops, interactive office hours, or watch on-demand tech talks at your own pace. Join us to fuel your learning journey with AWS.
#AWS
- Category
- AWS Developers
- Tags
- Amazon SageMaker, Amazon SageMaker Clarify, Machine Learning

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