Machine learning is an important part of the data science field. In petrophysics, machine learning algorithms and applications have been widely approached. In this context, Vietnam Petroleum Institute (VPI) has researched and deployed several effective prediction models, namely missing log prediction, fracture zone and fracture density forecast, etc. As one of our solutions, VPI-MLogs is a web-based deployment platform which integrates data preprocessing, exploratory data analysis, visualisation and model execution. Using the most popular data analysis programming language, Python, this approach gives users a powerful tool to deal with the petrophysical logs section. The solution helps to narrow the gap between common knowledge and petrophysics insights. This article will focus on the web-based application which integrates many solutions to grasp petrophysical data.

As generative AI continues to evolve, educators face the challenge of preparing students for a future where AI-assisted work is integral to professional success. This paper introduces ChatISA, an in-house, multi-model chatbot designed to support students in an Information Systems and Analytics department. ChatISA comprises four primary modules-Coding Companion, Project Coach, Exam Ally, and Interview Mentor-each tailored to enhance different aspects of the educational experience. Through iterative development, student feedback, and leveraging open-source frameworks, we created a robust tool that addresses coding inquiries, project management, exam preparation, and interview readiness. The implementation of ChatISA revealed significant insights and challenges, including the necessity of ethical guidelines and balancing AI usage with maintaining student agency. Our findings underscore the importance of adaptive pedagogy and proactive engagement with AI tools to maximize their educational benefits. To support broader adoption and innovation, all code for ChatISA is made publicly available on GitHub, enabling other institutions to customize and integrate similar AI-driven educational tools within their curricula.

In this paper, we are going to share a draft of the development of a conversational agent created to disseminate information about historical sites located in the Seoul. The primary objective of the agent is to increase awareness among visitors who are not familiar with Seoul, about the presence and precise locations of valuable cultural heritage sites. It aims to promote a basic understanding of Korea's rich and diverse cultural history. The agent is thoughtfully designed for accessibility in English and utilizes data generously provided by the Seoul Metropolitan Government. Despite the limited data volume, it consistently delivers reliable and accurate responses, seamlessly aligning with the available information. We have meticulously detailed the methodologies employed in creating this agent and provided a comprehensive overview of its underlying structure within the paper. Additionally, we delve into potential improvements to enhance this initial version of the system, with a primary emphasis on expanding the available data through our prompting. In conclusion, we provide an in-depth discussion of our expectations regarding the future impact of this agent in promoting and facilitating the sharing of historical sites.

At its core, feather was a tool that allowed model developers to build shareable user interfaces for their models in under 20 lines of code. Using the Python SDK, developers specified visual components that users would interact with. (e.g. a FileUpload component to allow users to upload a file). Our service then provided 1) a URL that allowed others to access and use the model visually via a user interface; 2) an API endpoint to allow programmatic requests to a model. In this paper, we discuss feather's motivations and the value we intended to offer AI researchers and developers. For example, the SDK can support multi-step models and can be extended to run automatic evaluation against held out datasets. We additionally provide comprehensive technical and implementation details. N.B. feather is presently a dormant project. We have open sourced our code for research purposes: https://github.com/feather-ai/

Streamlit is a powerful and user-friendly open-source Python library that makes it easier to build interactive web applications for machine learning and data science. With Streamlit, developers and data scientists can create engaging, informative, and visually appealing apps with just a few lines of code. One of the main benefits of Streamlit is its simplicity. You can write your code in a familiar environment and use the library's high-level APIs to quickly build complex and interactive web applications. Whether you're a seasoned software engineer or a beginner in data science, Streamlit makes it easy to get started.

Machine Learning is a fast-growing field, and its applications have become ubiquitous in our day-to-day lives. As the demand for ML models increases, so makes the demand for user-friendly interfaces to interact with these models. This blog is a tutorial for building intuitive frontend interfaces for Machine Learning models using two popular open-source libraries – Streamlit vs. Gradio. Streamlit is a python library for building data-driven applications specifically designed for machine learning and data science. It makes it easy to create a frontend UI in just a short amount of time with multiple features. On the other hand, Gradio is a library for Machine Learning models that makes it possible to quickly and easily create web-based interfaces for your models.

When it comes to making sure our homes are clean and healthy, one of the most important things to consider is our indoor air quality. Poor air quality can cause a range of health issues, from respiratory illnesses to allergies and asthma. So how can we improve our air quality and keep our homes healthy? The first step to improving indoor air quality is to reduce the amount of pollutants in your home. This can include anything from dust, pet dander, or smoke.

For every machine learning enthusiast, we were told to go to Kaggle. Should it end at just making predictions on the test data and submitting it? Or we would always be supplied with excel sheets to make predictions on with the model in real-life scenarios. What do we need to do? Read along to find out what needs to be done. Kowepe bank of Nigeria conducted marketing campaigns via phone calls with their clients. These campaigns prompt their clients to subscribe to a specific financial product of the bank (term deposit).

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There are so many Machine Learning (ML) papers out there and lots more comes out every year. They become part of a huge collection of modules, optimization, argumentation, equations, algorithms, diagrams, etc. On top of it, there are an enormous amount of blog posts and newsletters. Paperswithcode/methods gives a good summary of what is out there. But I still easily get lost in this knowledge pool when trying to dev.