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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.

ML 271
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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.

ML 191
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Top 40 Python Libraries for AI, ML and Data Science

Analytics Vidhya

Known for its beginner-friendliness, you can dive into AI without complex code. This flexible language has you covered for all things AI and beyond. This article is […] The post Top 40 Python Libraries for AI, ML and Data Science appeared first on Analytics Vidhya. Python’s superpower?

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Build, Deploy, and Manage ML Models with Google Vertex AI

Analytics Vidhya

Vertex AI is a unified platform from Google Cloud offering tools and infrastructure to build, deploy, and manage machine learning models.

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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

When developing a Gen AI application, one of the most significant challenges is improving accuracy. 💥 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. .

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Scale Zeitgeist: AI Readiness Report

insideBIGDATA

Our friends over at Scale are excited to introduce the 2nd edition of Scale Zeitgeist: AI Readiness Report! The company surveyed more than 1,600 executives and ML practitioners to uncover what’s working, what’s not, and the best practices for organizations to deploy AI for real business impact.

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ML stack

Dataconomy

The ML stack is an essential framework for any data scientist or machine learning engineer. Understanding the components and benefits of an ML stack can empower professionals to harness the true potential of machine learning technologies. What is an ML stack?

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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.