Big Data

Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple targets, past covariates, and past-future covariates with no task-specific fine-tuning. It takes the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard. The weights, however, ship under a non-commercial, non-production license.

In this tutorial, we explore a LabPlot-inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis...

In this tutorial, we build a complete quantitative backtesting workflow with OctoBot and OctoBot-Script while keeping the environment isolated from Colab’s preinstalled dependencies. We...

Feyn Labs has released SQRL, a family of text-to-SQL models that inspect a database with read-only probes before committing to a query. The flagship SQRL-35B-A3B reports 70.6% execution accuracy on BIRD Dev, edging Claude Opus 4.6, and distills into self-hostable 4B and 9B checkpoints.

We build an end-to-end spatial graph learning pipeline using city2graph. We collect urban POI and street network data from OpenStreetMap, with a synthetic fallback for reliability. We engineer spatial features, construct several proximity graph families, and compare how each represents the same urban environment. We then build heterogeneous and homogeneous graphs, convert them to PyTorch Geometric, and train a GraphSAGE model to predict POI categories from spatial structure.

In this tutorial, we work with NVIDIA's Nemotron-Pretraining-Code-v3 dataset as a large-scale metadata index for code pretraining research. We stream the dataset instead of downloading it, inspect its schema, and build a manageable sample. We analyze languages, file extensions, repository frequency, and directory depth to understand the index structure. We then reconstruct raw GitHub URLs, fetch real source files, and estimate the token scale of the fetched code.

In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for...

In this tutorial, we will generate knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. We start by setting up the...

In this tutorial, we implement how to use pandas-ta-classic to build a complete technical analysis and trading strategy workflow. We start by installing the...

In this tutorial, we perform an advanced single-cell RNA-seq analysis workflow using Scanpy on the PBMC-3k benchmark dataset. We start by loading the dataset,...

Introducing NeuralSet: Meta's Simple, Fast, and Scalable Python Package That Bridges Neuroscience and AI

In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by...

LoRA is widely used for fine-tuning large models because it’s efficient, but it quietly assumes that all updates to a model are similar. In...

In this tutorial, we explore how we use BudouX to bring intelligent, phrase-aware line breaking to languages where whitespace is not naturally present, such...

In this tutorial, we explore Datashader, a powerful, high-performance visualization library for rendering massive datasets that quickly overwhelm traditional plotting tools. We work through...

In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with...

In this tutorial, we build a complete pipeline for single-cell RNA sequencing analysis using Scanpy. We start by installing the required libraries and loading...

At first glance, adding more features to a model seems like an obvious way to improve performance. If a model can learn from more...

In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker....

In this tutorial, we demonstrate how we use Ibis to build a portable, in-database feature engineering pipeline that looks and feels like Pandas but...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important:...

In this tutorial, we build an advanced interactive dashboard using Textual, and we explore how terminal-first UI frameworks can feel as expressive and dynamic...

In this tutorial, we explore how to harness Apache Spark's techniques using PySpark directly in Google Colab. We begin by setting up a local...

In this tutorial, we build a compact, efficient framework that demonstrates how to convert tool documentation into standardized, callable interfaces, register those tools in...

In this tutorial, we set out to build an advanced interactive dashboard using Dash, Plotly, and Bootstrap. We highlight not only how these tools...

LLMs have rapidly advanced with soaring parameter counts, widespread use of mixture-of-experts (MoE) designs, and massive context lengths. Models like DeepSeek-R1, LLaMA-4, and Qwen-3...

In the fast-paced world of Artificial Intelligence (AI) and Machine Learning, staying updated with the latest trends, breakthroughs, and discussions is crucial. Reddit, the...

A data warehouse is a data management system for data reporting, analysis, and storage. It is an enterprise data warehouse and is part of...

Extract, Transform, and Load are referred to as ETL. ETL is the process of gathering data from numerous sources, standardizing it, and then transferring...

Artificial Intelligence has limitless possibilities, which is truly evident from the new releases and developments it introduces everyone to. With the release of the...

The most prevalent type of data is tabular data. This form contains many datasets from surveys, censuses, and administrative sources. These datasets could include...

The most important frontline diagnostic tool for heart disease in the United States is echocardiography since it is portable, effective, and non-invasive while still...

Artificial intelligence has made waves throughout the past decade, where advancements are showing up in everyday applications. But getting there requires a ton of...

The phrase "data engineering tools" refers to a broad category of technologies that comprise the contemporary data stack. Modern data stacks require specialized technologies...

Velox, a unified execution engine, was recently developed and made publicly available by Meta in association with Intel, ByteDance, and Ahana. This function library...

Big data describes the large, challenging volumes of structured and unstructured data that inundate businesses daily. However, what organizations do with the data matters...

A multidisciplinary research team has created a device that consumes a fraction of the energy needed by current general-purpose AI computing platforms to run...

Information representation technologies and innovations are required to dissect various data metrics and make the best information-driven decisions in the world of big data....

Google has added a column-level encryption tool and dynamic masking of information to its Software as a service data repository BigQuery. These features help...

Data has become essential for businesses to comprehend and analyze underlying patterns, sales, and growth. One problem is that data-driven companies may employ inaccurate...

Tensors are an effective method for handling and representing multidimensional data arrays. However, they have a limitation in terms of storage and computation. Tensor...

This Article is written as a summary by Marktechpost Staff based on the research article 'Intel Labs Accelerates Single-cell RNA-Seq Analysis'. All Credit For...

As databases grow in size, queries become slower. Database queries frequently contain multiple repetitive procedures that can be eliminated. To discover a full name...

Recent developments in machine learning have resulted in more complicated predictive models, typically at the expense of interpretability. Interpretability is frequently required, especially in...

Naveed Ahmed Janvekar is a Senior Data Scientist working at Amazon in the United States. He works on solving fraud and abuse problems on...

Machine learning has been possible partly due to the accumulation of data, and within that data, an important step is that of data validation....