Unite.AI

JULY 25, 2023

In this process, the AI system's training data, model parameters, and algorithms are updated and improved based on input generated from within the system. Model Drift: The model’s predictive capabilities and efficiency decrease over time due to changing real-world environments. Let’s discuss this in more detail.

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IBM Journey to AI blog

DECEMBER 15, 2023

When AI algorithms, pre-trained models, and data sets are available for public use and experimentation, creative AI applications emerge as a community of volunteer enthusiasts builds upon existing work and accelerates the development of practical AI solutions. Morgan and Spotify.

AI Tools 207

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Unite.AI

JUNE 24, 2024

This is the reason why data scientists need to be actively involved in this stage as they need to try out different algorithms and parameter combinations. This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call data drift.

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Unite.AI

JUNE 13, 2023

A lot of the assumptions that you make that these algorithms are based on, when they go to the real world, they don't hold, and then you have to figure out how to deal with that. I think that a lot of the difference is that, one, engineering, safety and so on, and maybe the other one of course is that your assumptions don't hold.

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Viso.ai

MAY 21, 2024

Data science is a multidisciplinary field that relies on scientific methods, statistics, and Artificial Intelligence (AI) algorithms to extract knowledgable and meaningful insights from data. At its core, data science is all about discovering useful patterns in data and presenting them to tell a story or make informed decisions.

Data Science 52

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The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

ODSC - Open Data Science

OCTOBER 11, 2023

Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.

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