In this presentation, we explored how AWS Bedrock provides a framework for developing ethical and trustworthy AI systems. Bedrock guides companies through the full machine learning lifecycle from build to deployment and operations. The key pillars of Bedrock include educating teams about topics like bias, constructing diverse teams, thoroughly evaluating AI use cases, curating high-quality training data, testing for and mitigating algorithmic bias, enabling human review of AI where appropriate, and monitoring models on an ongoing basis. AWS offers services like SageMaker Clarify, Augmented AI, and Model Monitor that directly support these pillars and make it easier for developers to bake responsibility into their ML implementations. We provided examples of how global companies across sectors leverage Bedrock practices to create fair and transparent AI applications in areas like finance, hiring, and content moderation. Bedrock allows organizations to tap into the end-to-end machine learning capabilities of AWS while also ensuring their AI is ethical, explainable, and accountable. This combination of cutting-edge technology and responsible design establishes trust and confidence in AI systems. By reviewing the Bedrock whitepaper, engaging AWS's responsible AI experts, and adopting the prescribed capabilities, any organization can take a principled approach to AI innovation. With Bedrock's guardrails in place, companies can develop AI that augments human intelligence for the benefit of all.
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📊 Data Visualization and Python:
Data Visualization with Python: https://www.coursera.org/learn/data-visualization-python
🛠️ DevOps, MLOps, and Cloud Computing:
DevOps, DataOps, MLOps: https://www.coursera.org/learn/devops-dataops-mlops-duke
MLOps Platforms: AWS SageMaker and Azure ML: https://www.coursera.org/learn/mlops-aws-azure-duke
Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
🐍 Python Essentials:
Python Essentials for MLOps: https://www.coursera.org/learn/python-essentials-mlops-duke
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📚 Must-Read Books:
Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Developing on AWS with C#: https://www.amazon.com/Developing-AWS-Comprehensive-Solutions-Platform/dp/1492095877
Pragmatic AI Labs Books: https://www.amazon.com/gp/product/B0992BN7W8
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Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
noahgift.com: https://noahgift.com/
Pragmatic AI Labs Website: https://paiml.com/
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