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Transforming customer service: How generative AI is changing the game

IBM Journey to AI blog

Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. While traditional AI approaches provide customers with quick service, they have their limitations. Watsonx.ai

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Getting ready for artificial general intelligence with examples

IBM Journey to AI blog

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

Dagster Supports end-to-end data management lifecycle. Its software-defined assets (announced through Rebundling the Data Platform ) and built-in lineage make it an appealing tool for developers. Seamless integration with many data sources and destinations. Uses secure protocols for data security.

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Top Data Challenges Facing Modern Retailers

ODSC - Open Data Science

The solution for data quantity challenges in the retail industry lies in enhanced storage and management. Integrating software that can automatically categorize or process could solve the issue of being overwhelmed by information. For example, retailers could analyze and reveal trends much faster with a big data platform.

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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Data visualisation principles include clarity, accuracy, efficiency, consistency, and aesthetics. A bar chart represents categorical data with rectangular bars. In contrast, a histogram represents the distribution of numerical data by dividing it into intervals and displaying the frequency of each interval with bars.

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Data security: Why a proactive stance is best

IBM Journey to AI blog

An integrated data protection system can protect your assets by monitoring them, automating access control, setting up notifications, and auditing your password management. Put into place data protection tools such as data encryption algorithms, key management, redaction, data masking and erasure, and data resiliency.

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Use the Amazon SageMaker and Salesforce Data Cloud integration to power your Salesforce apps with AI/ML

AWS Machine Learning Blog

For more information about this process, refer to New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler. Although we use a specific algorithm to train the model in our example, you can use any algorithm that you find appropriate for your use case.

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