MindsDB VS scikit-learn

Customer support is the backbone of any successful business. In today's digital landscape, leveraging artificial intelligence (AI) to automate and enhance support experiences can set your product apart. In this article, we'll explore how to build a customer support application powered by MindsDB, an open-source AI platform that makes it easy to integrate machine learning into your apps.

MindsDB is an AI-powered SQL layer that lets you query machine learning models like databases. Their Knowledge Bases let you embed and semantically search unstructured data—like resumes.

Before going deeper into other details, let's first understand what MindsDB is and how it made it super easy to build the workflow and execute it. MindsDB is an AI Data Solution Platform that makes it easy to connect, unify, and respond (as stated on their site). Simply put, we can understand MindsDB as an abstraction (Hidden Layer) that handles the connection of various data sources and automatically creates pipelines from that data, so you don't need to worry about AI integration development. MindsDB made it easy, like plug and play. Now, you can focus more on business logic rather than other unnecessary stuff like building a chatbot that answers user queries (sadly, but it's the truth!).

Hi, In an era where building AI-powered applications often feels like assembling a spaceship from scratch, I discovered a different path — MindsDB. As a developer working on KbNet, I needed a way to automatically generate summaries of knowledge base articles using AI. Instead of setting up complex machine learning pipelines, I used MindsDB and built a working AI flow in hours — not days.

In this tutorial, you'll learn how to build a production-ready AI agent that can answer questions using both structured data from a PostgreSQL database and unstructured content from PDF files. We'll use MindsDB, an open-source platform designed to integrate LLM-powered agents with databases and external knowledge sources like documents.

Access open source MindsDB’s Federated Query Engine on GitHub here.

But this hacktoberfest I took a great step towards contributing to open source. During this hacktoberfest I have contributed to MindsDB which is a great platform for building AI from enterprise data, enabling smarter organizations.

1️⃣ Quira Hacktoberfest with MindsDB 🐻‍❄️

Database Connection: Ollama supports several data platforms.

For apps demanding robust machine learning capabilities, frameworks like TensorFlow provide the scalability and flexibility needed to handle large-scale data and models. These tools are essential for developers building features like recommendation engines or predictive analytics.

Machine learning (ML) teaches computers to learn from data, like predicting user clicks. Start with simple models like regression (predicting numbers) and clustering (grouping data). Deep learning uses neural networks for complex tasks, like image recognition in a Vue.js gallery. Tools like Scikit-learn and PyTorch make it easier.

Scikit-learn Documentation: https://scikit-learn.org/

7. Scikit-learn - Machine Learning

Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.

scikit-learn (optional): Useful for additional training or evaluation tasks.

The race condition bugs are typically hidden by different software layers. For instance, we found one that involves OpenBLAS's pthreads-based thread pool management and maybe its scipy bindings:

- https://github.com/scipy/scipy/issues/21479

it might be the same as this one that further involves OpenMP code generated by Cython:

- https://github.com/scikit-learn/scikit-learn/issues/30151

We haven't managed to write minimal reproducers for either of those but as you can observe, those race conditions can only be triggered when composing many independently developed components.

Visit the repository and explore examples.

How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations.

Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!