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How IBM and AWS are partnering to deliver the promise of AI for business

IBM Big Data Hub

Real-time data analytics helps in quick decision-making, while advanced forecasting algorithms predict product demand across diverse locations. AWS’s scalable infrastructure allows for rapid, large-scale implementation, ensuring agility and data security.

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Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store

AWS Big Data

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. To get started, we need an Amazon Redshift Serverless data warehouse with the Redshift ML feature enabled and an Amazon SageMaker Studio environment with access to SageMaker Feature Store.

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Better, faster decisions: Why businesses thrive on real-time data

CIO Business Intelligence

“The enormous potential of real-time data not only gives businesses agility, increased productivity, optimized decision-making, and valuable insights, but also provides beneficial forecasts, customer insights, potential risks, and opportunities,” said Krumova.

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10 everyday machine learning use cases

IBM Big Data Hub

ML also helps businesses forecast and decrease customer churn (the rate at which a company loses customers), a widespread use of big data. ML classification algorithms are also used to label events as fraud, classify phishing attacks and more. Antivirus programs may use AI and ML techniques to detect and block malware.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

You can subscribe to data products that help enrich customer profiles, for example demographics data, advertising data, and financial markets data. Amazon Kinesis ingests streaming events in real time from point-of-sales systems, clickstream data from mobile apps and websites, and social media data.

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Database Activity Monitoring – A Security Investment That Pays Off

Smart Data Collective

On the one hand, the use of agents allows you to actively monitor and respond to events. During this process, you need to analyze your data assets, categorize and prioritize them, conduct a risk assessment, and establish appropriate monitoring and response techniques. DAM market trends and forecasts.

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Capgemini and IBM Ecosystem strengthen partnership for Drone-as-a-Service

IBM Big Data Hub

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. billion by 2029, at a CAGR of 28.58% in the forecast period. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads.