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How to establish lineage transparency for your machine learning initiatives

IBM Journey to AI blog

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes.

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Data science

Dataconomy

Data engineering lays the groundwork by managing data infrastructure, while data preparation focuses on cleaning and processing data for analysis. Predictive analytics utilizes statistical algorithms and machine learning to forecast future outcomes based on historical data.

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Data scientist

Dataconomy

Data scientists play a crucial role in today’s data-driven world, where extracting meaningful insights from vast amounts of information is key to organizational success. Their work blends statistical analysis, machine learning, and domain expertise to guide strategic decisions across various industries.

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Responsible Citizen Data Science. Yes, it is Possible.

DataRobot Blog

To retain market leadership in the algorithm economy, enterprises require new ways to maximize the value of data and AI with citizen data scientists. Don’t think citizen data science is. by Jen Underwood. Read More.

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Using Graphs for Feature Engineering, Prompt Fine-Tuning for Generative AI, and Confident Data…

ODSC - Open Data Science

Sharda will walk through real-world examples, share code snippets, and explore how ARIMA Prophet compares when building models using Feature Engineering techniques and advanced machine learning algorithms.

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DataRobot Joins the AWS ISV Workload Migration Program

DataRobot Blog

DataRobot provides a Machine Learning platform that allows data scientists and citizen data scientists to quickly and efficiently prepare, build and evaluate many competing models in order to identify the optimal algorithm to solve the use case. Learn more. Learn More.

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Predicting the Future of Data Science

Pickl AI

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. As industries increasingly rely on data-driven insights, ethical considerations regarding data privacy and bias mitigation will become paramount.