Remove Cost-Benefit Remove Data Processing Remove Experimentation Remove Machine Learning
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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications.

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Amazon OpenSearch Service search enhancements: 2023 roundup

AWS Big Data

2023 was a year of rapid innovation within the artificial intelligence (AI) and machine learning (ML) space, and search has been a significant beneficiary of that progress. However, not all customers who have the opportunity to benefit from k-NN have adopted it, due to the significant engineering effort and resources required to do so.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. 46% of survey respondents in 2024 showed a preference for open source models.

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Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

Present a yummy spreadsheet that quantifies the cost of inaction , how much money you’ll lose by not delivering a 25% improvement every week. Yet, this incredible benefit was not a part of YouTube TV’s merchandizing strategy from day one. Social cues (/proof) can help create a sense of urgency for a whole host of companies.

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How to choose the best AI platform

IBM Big Data Hub

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality.

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New DataRobot and Snowflake Integrations: Seamless Data Prep, Model Deployment, and Monitoring

DataRobot Blog

We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production. Now data scientists can be agile across the machine learning life cycle with the benefit of Snowflake’s scale, security, and governance. Why are we focusing on this?

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively? pointed out in “ The Case for Learned Index Structures ” (see video ) the internal smarts (B-trees, etc.) of relational databases represent early forms of machine learning.

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