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MLOps and the evolution of data science

IBM Journey to AI blog

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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A comprehensive comparison of RPA and ML

Dataconomy

RPA is often considered a form of artificial intelligence, but it is not a complete AI solution. AI, on the other hand, can learn from data and adapt to new situations without human intervention. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

ML 133
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Dogs vs Cats Audio Classification

Mlearning.ai

Using PyTorch Deep Learning Framework and CNN Architecture Photo by Andrew S on Unsplash Motivation Build a proof-of-concept for Audio Classification using a deep-learning neural network with PyTorch framework. Data Source here. This is inherently a supervised learning problem.

Azure 52
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A comprehensive comparison of RPA and ML

Dataconomy

RPA is often considered a form of artificial intelligence, but it is not a complete AI solution. AI, on the other hand, can learn from data and adapt to new situations without human intervention. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data.

ML 70
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How To Use ML for Credit Scoring & Decisioning

phData

Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models. Machine learning in credit scoring and decisioning typically involves supervised learning , a type of machine learning where the model learns from labeled data.

ML 52
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Continual Learning: Methods and Application

The MLOps Blog

Important note: Continual learning aims to allow the model to effectively learn new concepts while ensuring it does not forget already acquired information. Plenty of CL techniques exist that are useful in various machine-learning scenarios. There is no incremental training and no continual learning.

ML 59
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How MLOps Work in the Era of Large Language Models

ODSC - Open Data Science

Large language models (LLMs) and generative AI have taken the world by storm, allowing AI to enter the mainstream and show that AI is real and here to stay. However, a new paradigm has entered the chat, as LLMs don’t follow the same rules and expectations of traditional machine learning models.