The MLOps Blog

JUNE 27, 2023

Model governance and compliance : They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. FMEval provides the ability to perform evaluations for both LLM model endpoints or the endpoint for a generative AI service as a whole. What is FMEval? Tomer Shenhar is a Product Manager at AWS.

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AWS Machine Learning Blog

OCTOBER 15, 2024

These advanced models from Bria AI generate high-quality and contextually relevant visual content that is ready to use in marketing, design, and image generation use cases across industries from ecommerce, media and entertainment, and gaming to consumer-packaged goods and retail. model using SageMaker JumpStart. Overview of Bria 2.3,

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AWS Machine Learning Blog

NOVEMBER 14, 2024

This includes: Risk assessment : Identifying and evaluating potential risks associated with AI systems. Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. Human oversight : Including human involvement in AI decision-making processes.

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ODSC - Open Data Science

JANUARY 10, 2024

Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short.

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Large Language Models LLM Deep Learning Data Science 52

Pickl AI

JULY 14, 2023

Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.

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Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Unite.AI

FEBRUARY 22, 2024

In the rapidly evolving realm of modern technology, the concept of ‘ Responsible AI ’ has surfaced to address and mitigate the issues arising from AI hallucinations , misuse and malicious human intent. Balancing AI progress with societal values is vital for meaningful technological advancements that benefit humanity.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

This post explains how the solution is built using Anthropic’s Claude 3.5 Rifat is also very involved in MLOps, FMOps and Responsible AI. Henry Wang is a senior applied scientist at the AWS Generative AI Innovation Center, where he researches and builds generative AI solutions for AWS customers.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Generative AI solutions often use Retrieval Augmented Generation (RAG) architectures, which augment external knowledge sources for improving content quality, context understanding, creativity, domain-adaptability, personalization, transparency, and explainability. Satish Sarapuri is a Sr.

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