Remove Data Processing Remove Experimentation Remove Machine Learning Remove Optimization
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DS Smith sets a single-cloud agenda for sustainability

CIO Business Intelligence

Its digital transformation began with an application modernization phase, in which Dickson and her IT teams determined which applications should be hosted in the public cloud and which should remain on a private cloud. Energy optimization is another key aspect of DS Smith’s data and sustainability pipeline, the CIO says. As for No.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools.

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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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Customer Experience and Emerging Technologies: My CXChat Summary on Artificial Intelligence, Machine Learning and the Customer

Business Over Broadway

I was invited as a guest in a weekly tweet chat that is hosted by Annette Franz and Sue Duris. According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT.

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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. AGI wouldn’t just perceive its surroundings; it would understand them.

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DataRobot Notebooks: Enhanced Code-First Experience for Rapid AI Experimentation

DataRobot Blog

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. A host of open-source libraries. Auto-scale compute.

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The DataOps Vendor Landscape, 2021

DataKitchen

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Collaboration and Sharing.

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