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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. Data Platforms. Data Integration and Data Pipelines. Data preparation, data governance, and data lineage.

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NLP Isn’t Enough. Leading Financial Services Companies Are Now Moving to Conversational AI.

CIO Business Intelligence

In some parts of the world, companies are required to host conversational AI applications and store the related data on self-managed servers rather than subscribing to a cloud-based service. Data integration can also be challenging and should be planned for early in the project. . Just starting out with analytics?

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5 Key Data Storage Trends to Watch For in 2021

CDW Research Hub

Not everyone is ready to move all their data to the cloud. As we move forward, hybrid cloud continues to be the data storage strategy that helps organizations gain cost-effectiveness and increase data mobility between on-premises, public cloud and private cloud without compromising data integrity.

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Why you should care about debugging machine learning models

O'Reilly on Data

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. That’s where remediation strategies come in. We discuss seven remediation strategies below. Data augmentation.

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Data Analytics for Crypto Casinos: Significance and Challenges

BizAcuity

Crypto casinos need a solid operation strategy, an analytics platform and a fail-safe mechanism in place. Moreover, there should be a powerful data management and analytics pipeline for operational usage. If you would like to strengthen your data and BI strategy, BizAcuity can help you with the same.

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How to accelerate your data monetization strategy with data products and AI

IBM Big Data Hub

Data monetization strategy: Managing data as a product Every organization has the potential to monetize their data; for many organizations, it is an untapped resource for new capabilities. Generative AI has only served to accelerate the options for data product design, lifecycle delivery and operational management.

Strategy 105
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AI In Analytics: Today and Tomorrow!

Smarten

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.