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Machine Learning Experiment Tracking Using MLflow

Analytics Vidhya

Introduction The area of machine learning (ML) is rapidly expanding and has applications across many different sectors. Keeping track of machine learning experiments using MLflow and managing the trials required to construct them gets harder as they get more complicated.

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A Beginners Guide to LLMOps For Machine Learning Engineering 

Analytics Vidhya

Introduction The release of OpenAI’s ChatGPT has inspired a lot of interest in large language models (LLMs), and everyone is now talking about artificial intelligence. But it’s not just friendly conversations; the machine learning (ML) community has introduced a new term called LLMOps.

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Building Invoice Extraction Bot using LangChain and LLM

Analytics Vidhya

For invoice extraction, one has to gather data, build a document search machine learning model, model fine-tuning etc. The introduction of Generative AI took all of us by storm and many things were simplified using the LLM model.

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Why There’s No Better Time to Learn LLM Development

Towards AI

However, a large amount of work has to be delivered to access the potential benefits of LLMs and build reliable products on top of these models. This work is not performed by machine learning engineers or software developers; it is performed by LLM developers by combining the elements of both with a new, unique skill set.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. This is why Machine Learning Operations (MLOps) has emerged as a paradigm to offer scalable and measurable values to Artificial Intelligence (AI) driven businesses.

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Researchers at Stanford Introduces LLM-Lasso: A Novel Machine Learning Framework that Leverages Large Language Models (LLMs) to Guide Feature Selection in Lasso ℓ1 Regression

Marktechpost

Researchers from Stanford University and the University of Wisconsin-Madison introduce LLM-Lasso, a framework that enhances Lasso regression by integrating domain-specific knowledge from LLMs. Unlike previous methods that rely solely on numerical data, LLM-Lasso utilizes a RAG pipeline to refine feature selection.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Similar to how a customer service team maintains a bank of carefully crafted answers to frequently asked questions (FAQs), our solution first checks if a users question matches curated and verified responses before letting the LLM generate a new answer. No LLM invocation needed, response in less than 1 second.

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