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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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Generative AI, LLMs and AI Assistants: A Deep Dive into Customer Experience Technology

COPC

Understanding AI Terminology Generative AI: These are algorithms trained to predict data sequences based on training information. LLMs are unique because they have been trained on enormous data sets. They achieve this by training in vast amounts of data and iterative fine-tuning.

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

AWS Machine Learning

This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. The endpoints are then registered to the Salesforce Data Cloud to activate predictions in Salesforce. To use this dataset in your Data Cloud, refer to Create Amazon S3 Data Stream in Data Cloud.

APIs 78
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The Future of Debt Collection Agencies: Contact Center Technology and Customer-Centric Strategies

NobelBiz

Traditional collection methods, such as persistent phone calls and letters, are making way for more nuanced, technology-driven, and customer-oriented strategies. This evolution reflects broader trends in consumer behavior, regulatory environments, and technological advancements.

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Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

AWS Machine Learning

If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. Data Wrangler supports standard data types such as CSV, JSON, ORC, and Parquet. For more information, see Jupyter Kernel Gateway.

Scripts 80
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Build custom code libraries for your Amazon SageMaker Data Wrangler Flows using AWS Code Commit

AWS Machine Learning

Therefore, organizations have adopted technology best practices, including microservice architecture, MLOps, DevOps, and more, to improve delivery time, reduce defects, and increase employee productivity. This post introduces a best practice for managing custom code within your Amazon SageMaker Data Wrangler workflow.

Scripts 62
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock automates synchronization of your data with your vector store, including diffing the data when it’s updated, document loading, and chunking, as well as semantic embedding. RAG is a popular technique that combines the use of private data with large language models (LLMs).

APIs 112