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Build a Streamlit app integrating Deepchecks test suites and pandas-profiling reports for data validation and analysis. Learn to create interactive data quality checks and visualizations.
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Hands-on tutorial for developing test suites with Deepchecks, covering data validation, distribution analysis, custom tests, and model validation for machine learning projects.
Build a music analytics app using Python, Streamlit, and Spotify API. Learn to integrate song features, recommendations, album art, and artist data for a comprehensive music exploration experience.
Develop a music analytics application using Python and Spotify API. Learn to access audio features, perform track analysis, and create personalized recommendations for a unique music experience.
Learn audio processing in Python using librosa for feature extraction, beat detection, and spectral analysis. Explore practical techniques for music information retrieval and machine learning applications.
Comprehensive guide to integrating Nivo graphs into CoreUI React template, covering various chart types, customization, and implementation steps for creating interactive data visualizations.
Learn to use Hindi on Android for typing and speech-to-text, with tips for optimal results and content management using Google Drive and Docs. Ideal for creating literature and poetry on mobile devices.
Explore Neptune.ai for ML experiment tracking and model registry. Learn to log, compare, and organize model metadata efficiently, enhancing productivity and reproducibility in machine learning projects.
Explore DataRobot AI Cloud's MLOps capabilities, including model deployment, registry, and inference. Learn to deploy models from leaderboards and consume them via API, stream, local mode, and job workflows.
Comprehensive tutorial on DataRobot AI Cloud Platform, covering machine learning model building, evaluation, and deployment. Explores key features like AutoML, bias detection, and model optimization.
Learn to validate, test, and document data quality using great_expectations. Covers installation, initialization, context, demos with various datasets, and practical applications for data teams.
Learn to generate detailed, interactive dataset reports using pandas-profiling. Explore column details, interactions, correlations, and more with just a few lines of code.
Learn to integrate Chakra UI frontend with Quarkus Java backend in this hands-on tutorial. Covers RESTful API implementation, proxy setup, and data rendering for full-stack development.
Learn to integrate CoreUI frontend with Quarkus Java backend in this comprehensive tutorial. Covers API creation, proxy setup, RESTful requests, and data rendering for full-stack development.
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