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How companies around the world apply machine learning

O'Reilly Media - Data

The growing role of data and machine learning cuts across domains and industries. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machine learning and AI). Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly Media - Ideas

In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. Machine Learning model lifecycle management. Deep Learning. Data Platforms. Data Integration and Data Pipelines.

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How to take machine learning from exploration to implementation

O'Reilly Media - Data

Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Scalable Machine Learning for Data Cleaning.

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5 hot IT budget investments — and 2 going cold

CIO

This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%). CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%).

Budget 363
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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. AI continues to transform customer engagements and interactions with chatbots that use predictive analytics for real-time conversations.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases is completely serverless, so you don’t need to manage any infrastructure, and when using Knowledge Bases, you’re only charged for the models, vector databases and storage you use. Proper handling of specialized terminology and concepts in different formats is essential to detect insights and ensure analytical integrity.