Integrating Scikit-Learn and Statsmodels for Regression

Statistics and Machine Learning both aim to extract insights from data, though their approaches differ significantly. Traditional statistics primarily concerns itself with inference, using the entire dataset to test hypotheses and estimate probabilities about a larger population. In contrast, machine learning emphasizes prediction and decision-making, typically employing a train-test split methodology where models learn from …

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Can Chatbots Reduce Business Costs Dramatically? Expert Opinion and Insights

The clock strikes 3 AM. A potential buyer is browsing your website, eager to make a purchase, but they have a question. Your support team is fast asleep, and the sale slips away. Sound familiar? Today’s consumers expect instant gratification, and companies are feeling the pressure — both on their resources and their bottom line. Meeting these …

BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks

This paper was accepted at IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2024 Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, …

Investigation of a Cross-regional Network Performance Issue

Hechao Li, Roger Cruz Cloud Networking Topology Netflix operates a highly efficient cloud computing infrastructure that supports a wide array of applications essential for our SVOD (Subscription Video on Demand), live streaming and gaming services. Utilizing Amazon AWS, our infrastructure is hosted across multiple geographic regions worldwide. This global distribution allows our applications to deliver content …

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Build an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation

Retrieval Augmented Generation (RAG) is a state-of-the-art approach to building question answering systems that combines the strengths of retrieval and foundation models (FMs). RAG models first retrieve relevant information from a large corpus of text and then use a FM to synthesize an answer based on the retrieved information. An end-to-end RAG solution involves several …