Data Modeling, Decision Trees and Hadoop

Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

Machine Learning

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data.

Machine Learning

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Data Science Current

Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

Data science vs. machine learning: What’s the difference?

Must-Have Skills for a Machine Learning Engineer

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