Machine Learning

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A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing

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In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and […]

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JPMorgan expands AI investment as tech spending nears $20B

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Artificial intelligence is moving from pilot projects to core business systems inside large companies. One example comes from JPMorgan Chase, where rising AI investment is helping push the bank’s technology budget toward about US$19.8 billion in 2026. The spending plan reflects a broader shift among large enterprises. AI is no longer treated as a small

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YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency

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How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 1T total parameters and 68.8B activated parameters. The model architecture is designed to optimize performance

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Time Series Cross-Validation: A Guide to Techniques & Practical Implementation

ai, AI (Artificial Intelligence), Artificial Intelligence, Beginner, Machine Learning

Time series data drives forecasting in finance, retail, healthcare, and energy. Unlike typical machine learning problems, it must preserve chronological order. Ignoring this structure leads to data leakage and misleading performance estimates, making model evaluation unreliable. Time series cross-validation addresses this by maintaining temporal integrity during training and testing. In this article, we cover essential

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A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Design, Machine Learning, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, Systems design

Many engineering challenges come down to the same headache — too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be costly, and there may be hundreds of variables that could matter.Consider car safety design. Engineers must integrate thousands of

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Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations

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Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But do you know what it has actually learned? A team of University of Cambridge researchers propose ‘SymTorch’, a library designed to integrate symbolic regression (SR) into

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Google Drops Gemini 3.1 Flash-Lite: A Cost-efficient Powerhouse with Adjustable Thinking Levels Designed for High-Scale Production AI

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Google has released Gemini 3.1 Flash-Lite, the most cost-efficient entry in the Gemini 3 model series. Designed for ‘intelligence at scale,’ this model is optimized for high-volume tasks where low latency and cost-per-token are the primary engineering constraints. It is currently available in Public Preview via the Gemini API (Google AI Studio) and Vertex AI.

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The Top 10 LLM Evaluation Tools

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LLM evaluation tools help teams measure how a model performs across various tasks, including reasoning, summarization, retrieval, coding, and instruction-following. They analyze performance trends, detect hallucinations, validate outputs against ground truth, and benchmark improvements during fine-tuning or prompt engineering. Without robust evaluation frameworks, organizations risk deploying unpredictable or harmful AI systems. How LLM Evaluation Tools

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A Coding Guide to Build a Scalable End-to-End Analytics and Machine Learning Pipeline on Millions of Rows Using Vaex

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory. We generate a realistic, large-scale dataset, engineer rich behavioral and city-level features using lazy expressions and approximate statistics, and aggregate insights at scale. We then integrate Vaex with scikit-learn

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How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown

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In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract

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