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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. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Not least is the broadening realization that ML models can fail.

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Efficiently train models with large sequence lengths using Amazon SageMaker model parallel

AWS Machine Learning - AI

Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. This approach reduces memory pressure and enables efficient training of large models.

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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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The dawn of agentic AI: Are we ready for autonomous technology?

CIO

Ive spent more than 25 years working with machine learning and automation technology, and agentic AI is clearly a difficult problem to solve. One of the best is a penetration test that checks for ways someone could access a network. Could it work through complex, dynamic branch points, make autonomous decisions and act on them?

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Machine learning model serving architectures

Xebia

After months of crunching data, plotting distributions, and testing out various machine learning algorithms you have finally proven to your stakeholders that your model can deliver business value. For the sake of argumentation, we will assume the machine learning model is periodically trained on a finite set of historical data.

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Nvidia’s ‘hard pivot’ to AI reasoning bolsters Llama models for agentic AI

CIO

The company has post-trained its new Llama Nemotron family of reasoning models to improve multistep math, coding, reasoning, and complex decision-making. Post-training is a set of processes and techniques for refining and optimizing a machine learning model after its initial training on a dataset.

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5 dead-end IT skills — and how to avoid becoming obsolete

CIO

CIOs and other executives identified familiar IT roles that will need to evolve to stay relevant, including traditional software development, network and database management, and application testing. In software development today, automated testing is already well established and accelerating.