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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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The future of data: A 5-pillar approach to modern data management

CIO

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

Data 167
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What is data analytics? Analyzing and managing data for decisions

CIO

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics methods and techniques.

Analytics 203
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Pentaho Continues To Innovate: Data Science Pack Operationalizes Use of R and Weka

CTOvision

SAN JOSE, Calif. , June 3, 2014 /PRNewswire/ – Hadoop Summit – According to the O’Reilly Data Scientist Salary Survey , R is the most-used tool for data scientists, while Weka is a widely used and popular open source collection of machine learning algorithms. Product Availability.

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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning - AI

These include batch processing strategies, LoRA configuration settings, and prompt masking techniques that improved fine-tuned model performance by up to 5% compared to open-source fine-tuning recipe performance. Behind-the-scenes optimizations Through extensive experimentation, weve optimized implementations of Meta Llama 3.2

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Forget the Rules, Listen to the Data

Hu's Place - HitachiVantara

Rule-based fraud detection software is being replaced or augmented by machine-learning algorithms that do a better job of recognizing fraud patterns that can be correlated across several data sources. DataOps is required to engineer and prepare the data so that the machine learning algorithms can be efficient and effective.

Data 90
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You can no longer afford time amnesia in your software systems.

The Agile Monkey

Event-driven machine learning will enable a new generation of businesses that will be able to make incredibly thoughtful decisions faster than ever, but is your data ready to take advantage of it? Do you need help adopting event-sourcing or AI models at your organization? Get in touch with us!