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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. Every library has its own purpose and benefits.

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

IBM Big Data Hub

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 105
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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139
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AI in marketing: How to leverage this powerful new technology for your next campaign

IBM Big Data Hub

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

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Autoscaling Deployment with MLOps

DataRobot Blog

In this technical post, we’ll focus on some changes we’ve made to allow custom models to operate as an algorithm on Algorithmia, while still feeding predictions, input, and other metrics back to the DataRobot MLOps platform —a true best of both worlds. Data Science Expertise Meets Scalability.

Metrics 52
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MLOps and the evolution of data science

IBM Big Data Hub

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning.

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Conversational AI use cases for enterprises

IBM Big Data Hub

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. The technology’s ability to adapt and learn from interactions further refines customer support metrics, including response time, accuracy of information provided, customer satisfaction and problem-resolution efficiency.