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Explainable Artificial Intelligence (XAI) for AI & ML Engineers

Analytics Vidhya

Introduction Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing filed, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects […].

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Data Science vs. Machine Learning: What’s the Difference?

Marktechpost

In today’s tech-driven world, data science and machine learning are often used interchangeably. This article explores the differences between data science vs. machine learning , highlighting their key functions, roles, and applications. What is Machine Learning? However, they represent distinct fields.

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OpenAI Researchers Introduce MLE-bench: A New Benchmark for Measuring How Well AI Agents Perform at Machine Learning Engineering

Marktechpost

Machine Learning (ML) models have shown promising results in various coding tasks, but there remains a gap in effectively benchmarking AI agents’ capabilities in ML engineering. MLE-bench is a novel benchmark aimed at evaluating how well AI agents can perform end-to-end machine learning engineering.

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ML Engineering is Not What You Think — ML Jobs Explained

Towards AI

Last Updated on April 11, 2024 by Editorial Team Author(s): Boris Meinardus Originally published on Towards AI. How much machine learning really is in ML Engineering? There are so many different data- and machine-learning-related jobs. Join thousands of data leaders on the AI newsletter.

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The 7 Most Important Skill Sets for ML Engineers

Towards AI

Last Updated on July 3, 2024 by Editorial Team Author(s): Boris Meinardus Originally published on Towards AI. What are the most important skills for an ML Engineer? So, in this blog post, I want to share the 7 most important skill sets you might need to know if you want to become an ML engineer.

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Optimizing Energy Efficiency in Machine Learning ML: A Comparative Study of PyTorch Techniques for Sustainable AI

Marktechpost

The study outlines key concepts for understanding AI and sustainability, focusing on model-centric optimization tactics to reduce the environmental impact of ML. Inference, which accounts for 90% of ML costs, is a key area for energy optimization. compile balanced accuracy and energy efficiency.

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We employed ChatGPT as an ML Engineer. This is what we learned

Towards AI

Author(s): Eric Landau, Co-founder and CEO, Encord TLDR; Among the proliferation of recent use cases using the AI application ChatGPT, we ask whether it can be used to make improvements in other AI systems. We test it on a practical problem in a modality of AI in which it was not trained, computer vision, and report the results.