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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

According to IBM’s latest CEO study , industry leaders are increasingly focusing on AI technologies to drive revenue growth, with 42% of retail CEOs surveyed banking on AI technologies like generative AI, deep learning, and machine learning to deliver results over the next three years.

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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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Of Muffins and Machine Learning Models

Cloudera

Support for multiple sessions within a project allows data scientists, engineers and operations teams to work independently alongside each other on experimentation, pipeline development, deployment and monitoring activities in parallel. To learn more about CML, head over to [link] or connect with us directly.

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The New Improved and Open GraphDB

Ontotext

By taking the open source approach, the Workbench can address a wider spectrum of use-cases, creating a higher value for clients and increasing the likelihood that specific non-generic features exist and have been developed to address the real-world problems facing the optimization of semantic data processing and management. The Plugins.

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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. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. Observe, optimize, and scale enterprise data pipelines. . DataMo – Datmo tools help you seamlessly deploy and manage models in a scalable, reliable, and cost-optimized way. Kubeflow — The Machine Learning Toolkit for Kubernetes.

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Evaluating Ray: Distributed Python for Massive Scalability

Domino Data Lab

this post on the Ray project blog ?. for reinforcement learning (RL), ? for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. Ray: Scaling Python Applications. asyncio ?,