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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 8] Data about individuals can be decoded from ML models long after they’ve trained on that data (through what’s known as inversion or extraction attacks, for example). ML security audits.

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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 During the training of Llama 3.1

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5 findings from O'Reilly's machine learning adoption survey companies should know

O'Reilly Media - Data

New survey results highlight the ways organizations are handling machine learning's move to the mainstream. As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work. What metrics are used to evaluate success?

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MLflow: A platform for managing the machine learning lifecycle

O'Reilly Media - Data

Although machine learning (ML) can produce fantastic results, using it in practice is complex. For example, Uber and Facebook have built Michelangelo and FBLearner Flow to manage data preparation, model training, and deployment. Machine learning workflow challenges. algorithm) to see whether it improves results.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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Introducing Cloudera Fine Tuning Studio for Training, Evaluating, and Deploying LLMs with Cloudera AI

Cloudera

Fine tuning involves another round of training for a specific model to help guide the output of LLMs to meet specific standards of an organization. Given some example data, LLMs can quickly learn new content that wasn’t available during the initial training of the base model. Build and test training and inference prompts.

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5 Machine Learning Models Every Data Scientist Should Know

The Crazy Programmer

From Google and Spotify to Siri and Facebook, all of them use Machine Learning (ML), one of AI’s subsets. Whatever your motivation, you’ve come to the right place to learn the basics of the most popular machine learning models. 5 Machine Learning Models Every Data Scientist Should Know.