Remove 2022 Remove Automation Remove Data Ingestion
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Want to be a hybrid cloud winner? The recipe for XaaS success

IBM Journey to AI blog

relative to 2022, with the highest being in infrastructure-as-a-service (at 23%) followed by software-as-a-service (SaaS, at 8.4%) and IT services (at 3%). Building confidence in safeguarding data Data is the lifeblood of modern businesses, but its movement must be safe and compliant.

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Celebrating 40 years of Db2: Running the world’s mission critical workloads

IBM Journey to AI blog

Built on decades of innovation in data security, scalability and availability, IBM Db2 keeps business applications and analytics protected, highly performant, and resilient, anywhere. was a significant leap forward in data management, empowering organizations to unlock the full potential of their data.

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How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker

AWS Machine Learning Blog

Regardless of the models used, they all include data preprocessing, training, and inference over several billions of records containing weekly data spanning multiple years and markets to produce forecasts. A fully automated production workflow The MLOps lifecycle starts with ingesting the training data in the S3 buckets.

ML 102
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Build well-architected IDP solutions with a custom lens – Part 6: Sustainability

AWS Machine Learning Blog

Customers across all industries run IDP workloads on AWS to deliver business value by automating use cases such as KYC forms, tax documents, invoices, insurance claims, delivery reports, inventory reports, and more. Effectively manage your data and its lifecycle Data plays a key role throughout your IDP solution.

IDP 107
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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Choose the car-data-ingestion-pipeline.

ML 113
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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

In order analyze the calls properly, Principal had a few requirements: Contact details: Understanding the customer journey requires understanding whether a speaker is an automated interactive voice response (IVR) system or a human agent and when a call transfer occurs between the two.

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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning Blog

Date Hour Market Location MW Price 11/7/2022 17 RT Energy LCIENEGA_6_N001 0 $0 11/7/2022 17 RT Energy LCIENEGA_6_N001 1.65 $80.79 11/7/2022 17 RT Energy LCIENEGA_6_N001 5.15 $105.34 11/7/2022 17 RT Energy LCIENEGA_6_N001 8 $230.15 This example represents our willingness to bid 1.65