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Demand forecasting at Getir built with Amazon Forecast

AWS Machine Learning Blog

Among those algorithms, deep/neural networks are more suitable for e-commerce forecasting problems as they accept item metadata features, forward-looking features for campaign and marketing activities, and – most importantly – related time series features. She has 12 years of software development and architecture experience.

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eSentire delivers private and secure generative AI interactions to customers with Amazon SageMaker

AWS Machine Learning Blog

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. eSentire used gigabytes of additional human investigation metadata to perform supervised fine-tuning on Llama 2. They needed no additional infrastructure for data integration.

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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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10 Data Modeling Tools You Should Know

Pickl AI

Increased Efficiency And hands work productivity is the key objective of any developer. With the use of these tools, one can streamline the data modelling process. Moreover, these tools are designed to automate tasks like generating SQL scripts, documenting metadata and others. Thus, helping maintenance of data integrity.

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Introducing generative AI troubleshooting for Apache Spark in AWS Glue (preview)

Flipboard

How generative AI troubleshooting for Spark works For Spark jobs, the troubleshooting feature analyzes job metadata, metrics and logs associated with the error signature of your job to generates a comprehensive root cause analysis. Vishal Kajjam is a Software Development Engineer on the AWS Glue team.

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How we built our AI Lakehouse

AssemblyAI

In the course of developing our Conformer and Universal speech recognition models , we've had to navigate the complexities of handling massive amounts of audio data and metadata. As our data needs grew, so too did the accompanying challenges, such as fragmentation, bottlenecks, and limited accessibility.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

Flipboard

This emergent ability in LLMs has compelled software developers to use LLMs as an automation and UX enhancement tool that transforms natural language to a domain-specific language (DSL): system instructions, API requests, code artifacts, and more. He currently is working on Generative AI for data integration.

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