mljar-supervised is an Automated Machine Learning Python package for tabular data. It automates data preprocessing, model construction and hyperparameter tuning, and produces a Markdown report for each model. It offers Explain, Perform and Compete modes, along with model explanations and generated web apps for predictions.
Data Science at Home is a podcast about machine learning, artificial intelligence, and algorithms. The site describes it as among the top AI and machine learning podcasts in the world and notes that industry leaders have sponsored its initiative.
ML-Ops is a site about Machine Learning Operations. It covers motivation for MLOps, designing ML-powered software, the end-to-end ML workflow lifecycle, three levels of ML-based software, MLOps principles, CRISP-ML(Q), the MLOps Stack Canvas and ML model governance. It describes MLOps as aiming to unify the release cycle for machine learning and software.
Bokeh is a Python library for creating interactive plots, dashboards, and data applications in modern web browsers. Plots can go in Jupyter notebooks, be saved as standalone HTML, or run with live Python callbacks through the Bokeh server or embedded in an ASGI app. It includes tools for pan, zoom, hover, and selection.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
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Data Science at Home is a podcast about machine learning, artificial intelligence, and algorithms. The site describes it as among the top AI and machine learning podcasts in the world and notes that industry leaders have sponsored its initiative.
ML-Ops is a site about Machine Learning Operations. It covers motivation for MLOps, designing ML-powered software, the end-to-end ML workflow lifecycle, three levels of ML-based software, MLOps principles, CRISP-ML(Q), the MLOps Stack Canvas and ML model governance. It describes MLOps as aiming to unify the release cycle for machine learning and software.
Bokeh is a Python library for creating interactive plots, dashboards, and data applications in modern web browsers. Plots can go in Jupyter notebooks, be saved as standalone HTML, or run with live Python callbacks through the Bokeh server or embedded in an ASGI app. It includes tools for pan, zoom, hover, and selection.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
