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How to Choose Best ML Model for your Usecase?

Analytics Vidhya

Machine learning (ML) has become a cornerstone of modern technology, enabling businesses and researchers to make data-driven decisions with greater precision. However, with the vast number of ML models available, choosing the right one for your specific use case can be challenging. appeared first on Analytics Vidhya.

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ML and AI Model Explainability and Interpretability

Analytics Vidhya

Through tools like LIME and SHAP, we demonstrate how to gain insights […] The post ML and AI Model Explainability and Interpretability appeared first on Analytics Vidhya.

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On Device Llama 3.1 with Core ML

Machine Learning Research at Apple

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs).

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Machine Learning Made Simple for Data Analysts with BigQuery ML

KDnuggets

Thanks to tools like BigQuery ML, you can harness the power of ML without needing a computer science degree. Let's explore how to get started.

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Improving the Accuracy of Generative AI Systems: A Structured Approach

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. The number of use cases/corner cases that the system is expected to handle essentially explodes.

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ML interpretability

Dataconomy

ML Interpretability is a crucial aspect of machine learning that enables practitioners and stakeholders to trust the outputs of complex algorithms. What is ML interpretability? ML interpretability refers to the capability to understand and explain the factors and variables that influence the decisions made by machine learning models.

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Apache Iceberg vs Delta Lake vs Hudi: Best Open Table Format for AI/ML Workloads

Analytics Vidhya

If you’re working with AI/ML workloads(like me) and trying to figure out which data format to choose, this post is for you.

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Holding onto old BI technology while everything else moves forward is holding back organizations.