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

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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. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product.

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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions

ODSC - Open Data Science

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning Blog

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. The robust security features provided by Amazon S3, including encryption and durability, were used to provide data protection.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 1

AWS Machine Learning Blog

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

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Data virtualization unifies data for seamless AI and analytics

IBM Journey to AI blog

Despite heavy investments in databases and technology, many companies struggle to extract further value from their data. Data virtualization bridges this gap, allowing organizations to use their existing data sources with flexibility and efficiency for AI and analytics initiatives.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

AWS 95
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Solving Complex Telecom Challenges with Data Governance and Location Analytics

Precisely

For instance, telcos are early adopters of location intelligence – spatial analytics has been helping telecommunications firms by adding rich location-based context to their existing data sets for years. Despite that fact, valuable data often remains locked up in various silos across the organization.