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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 80
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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. Deploy the solution You can use the following AWS CloudFormation template to deploy the solution.

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Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store

AWS Big Data

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. To get started, we need an Amazon Redshift Serverless data warehouse with the Redshift ML feature enabled and an Amazon SageMaker Studio environment with access to SageMaker Feature Store.

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

A new paradigm in reporting and analysis is emerging. There was always a delay between the events being recorded in financial systems (for example, the purchase of a product or service) and the ability to put that information in context and draw useful conclusions from it (for example, a weekly sales report).

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Advancing Data Security With IBM Security Guardium Insights for IBM Cloud Pak for Security

CDW Research Hub

This provides near-real-time data activity monitoring and protection capabilities for Database as a Service (DBaaS) sources, such as AWS Kinesis and Azure Event Hubs. GDP is a leading data security platform for databases and data warehouses. Automated workflows and long-term data storage. Better together.

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Data Lakes: What Are They and Who Needs Them?

Jet Global

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

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Build a serverless analytics application with Amazon Redshift and Amazon API Gateway

AWS Big Data

Business teams can gain meaningful insights by simplifying their reporting through web applications and distributing it to a broader audience. Reporting and analysis – An application where you can trigger large analytical queries with dynamic inputs and then view or download the results. What are WebSockets and why do we need them?