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Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

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Read the original article at Turing Post , the newsletter for over 90 000 professionals who are serious about AI and ML. But newer datasets—such as Amazon’s, Criteo’s, and now Yambda—offer the kind of scale and nuance needed to push models from academic novelty to real-world utility.

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MLFlow Mastery: A Complete Guide to Experiment Tracking and Model Management

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This ensures smooth production processes. Managing ML projects without MLFlow is challenging. MLFlow Projects MLflow Projects enable reproducibility and portability by standardizing the structure of ML code. Document and Test : Keep thorough documentation and perform unit tests on ML workflows. Why Use MLFlow?

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Serve Machine Learning Models via REST APIs in Under 10 Minutes

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Pro New ChatGPT and Whisper APIs from OpenAI Our Top 5 Free Course Recommendations --> Get the FREE ebook The Great Big Natural Language Processing Primer and The Complete Collection of Data Science Cheat Sheets along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

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It may also be sent directly to dashboards, APIs, or ML models. Its key goals are to store data in a format that supports fast querying and scalability and to enable real-time or near-real-time access for decision-making. By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: No, thanks!

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7 Python Statistics Tools That Data Scientists Actually Use in 2025 - KDnuggets

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More On This Topic 7 Python Errors That Are Actually Features Math Myths Busted: What Beginners Actually Need for Data Science Free Courses That Are Actually Free: Data Analytics Edition What I Actually Do As a Data Scientist (in 2024) What Junior ML Engineers Actually Need to Know to Get Hired?

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10 GitHub Awesome Lists for Data Science

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Awesome Machine Learning: The Best ML Libraries Link: josephmisiti/awesome-machine-learning A comprehensive and organized list of machine learning frameworks, libraries, and software across multiple languages. It also includes free machine learning books, courses, blogs, newsletters, and links to local meetups and communities.

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Generative AI: A Self-Study Roadmap

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Quality Evaluation and Testing : Unlike traditional ML models with clear accuracy metrics, evaluating generative AI requires more sophisticated approaches. Understanding how different models tokenize text helps you estimate costs accurately and design efficient prompting strategies.

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