Year: 2026

Comprehensive overview of open-source AutoML tools in 2026, including active projects, maintenance status, tabular AutoML, foundation models, and agent-based AutoML systems.

I asked AI to load a CSV file for a medical data analysis use case. The code looked correct, but the dataframe was wrong. This is why checking AI output is so important.

AI generated a perfect data analysis report—but without visible code and workflow, it’s hard to trust the results. Here’s why transparency matters.

We describe how conversational notebook works in MLJAR Studio. It is a virtual AI Data Analyst that can answer data analysis questions using Python behind scenes. It was created on top of Jupyter notebook but has user frinedly design and is AI powered.

Use AI in healthcare without breaking HIPAA. Learn a safe workflow with MLJAR Studio, data anonymization, and practical examples to protect patient data and stay compliant.

Generate structured AutoML reports in Python that are easy to parse, LLM-friendly, and perfect for notebooks and automated analysis.

Compare the best AI coding assistants for data science in 2026: MLJAR Studio, GitHub Copilot, Cursor, Julius.ai, Deepnote, Hex, and ChatGPT. Pricing, privacy, offline capability, and AutoML — all in one guide.

Discover modern data analysis tools for pharmaceutical research, including private AI workflows and practical examples with MLJAR Studio.

Learn how AutoResearch by Andrej Karpathy works and how autonomous AI agents can run machine learning experiments. See a practical implementation with AutoLab.

Learn offline data analysis workflows in 2026. Compare local vs cloud, improve privacy, and use private AI tools for secure analytics.

Learn practical AI ethics for data science: fairness, bias detection, transparency, privacy, and responsible machine learning workflows.

Compare local vs cloud data processing for security, privacy, and compliance. Learn when private AI workflows are the safer choice.

Learn machine learning basics in this beginner-friendly guide. Understand key concepts, algorithms, and how to start building models with modern tools.

Learn the difference between Artificial Intelligence, Machine Learning, and Data Science. A practical guide with examples, workflows, and MLJAR tools.

Learn essential statistics for data science with practical Python examples: distributions, hypothesis testing, correlation, and regression.

Discover the essential Python libraries for modern data science in 2026—from NumPy and pandas to AutoML systems like mljar-supervised and AI-native environments like MLJAR Studio.

Discover how to use a private PyPI server for secure Python package installation via terminal, Jupyter Notebook, and Jupyter Package Manager.

Learn how to build a ChatGPT-style GIS application in a Jupyter Notebook using Python, geemap, OpenStreetMap, Mercury and a local LLM.

Learn how to build an AI chatbot in Python that talks to your PostgreSQL database using a local LLM (GPT-OSS) and Ollama. Generate SQL queries, tables, and charts - fully offline.

Zobacz jak zbudować aplikację chat w Pythonie z użyciem Mercury i polskiego modelu Bielik LLM. Krok po kroku: Ollama, notebook Jupyter, streaming odpowiedzi i lokalny chatbot AI.

Explore Mercury 3.0.0 updates for turning Python notebooks into web apps, with new features, improvements, and migration notes.