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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. However, as data volumes and complexity continue to grow, effective data governance becomes a critical challenge.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning

However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML workloads at scale. Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services.

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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. However, ML governance plays a key role to make sure the data used in these models is accurate, secure, and reliable.

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Guest Post: 6 Customer Service Trends That You’ll Want to Adopt in 2022

ShepHyken

With daily reports of data breaches, today’s consumers are more concerned about the security of their personal information than ever before. Confidentiality is a growing concern of governments and businesses. Therefore, it is a priority for companies receiving this data to protect and process this information responsibly. .

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Prescriptive analytics: The way forward for Big Data

Hero Digital

This, in a nutshell, is prescriptive analytics. For a long time, the field of data and analytics was focused on describing what happened — how many customers bought the product, what they looked like, how many came back, etc. With the advent of advanced ML algorithms, analytics has now entered the prescriptive phase.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines.

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Generate financial industry-specific insights using generative AI and in-context fine-tuning

AWS Machine Learning

Bond types**: The list covers a range of bond types, including corporate bonds, government bonds, high-yield bonds, and green bonds. In entered the Big Data space in 2013 and continues to explore that area. He is focused on Big Data, Data Lakes, Streaming and batch Analytics services and generative AI technologies.