Training large language models (LLMs) to act as autonomous agents for multi-turn, long-horizon tasks remains significant challenges in scalability and training efficiency. To address this, we introduce L-Zero (L0), a scalable, end-to-end training pipeline for general-purpose agents. Featuring a low-cost, extensible, and sandboxed concurrent agent worker pool, L0 lowers the barrier for applying reinforcement learning in complex environments. We also introduce NB-Agent, the agent scaffold within L0, which operates in a "code-as-action" fashion via a Read-Eval-Print-Loop (REPL). We evaluate L0 on factuality question-answering benchmarks. Our experiments demonstrate that a base model can develop robust problem-solving skills using solely Reinforcement Learning with Verifiable Rewards (RLVR). On the Qwen2.5-7B-Instruct model, our method boosts accuracy on SimpleQA from 30 % to 80 % and on HotpotQA from 22 % to 41 %. We have open-sourced the entire L0 system, including our L0 series models, the NB-Agent, a complete training pipeline, and the corresponding training recipes on (https://github.com/cmriat/l0).

Mathematical Optimization is getting more and more popular in most quantitative disciplines, such as engineering, management, economics, and operations research. Furthermore, Python is one of the most famous programming languages that is getting more attention nowadays. Therefore, we decided to create a course for mastering the development of optimization problems in the Python environment. Since this course is designed for all levels (from beginner to advanced), we start from the beginning that you need to formulate a problem. Therefore, after finishing this course, you will be able to find and formulate decision variables, objective function, constraints and define your parameters.

This article was published as a part of the Data Science Blogathon. It is a front-end framework that enables the use of Python in the browser. It is developed using Emscripten, Pyodide, WASM, and other modern web technologies. Using Python in the browser does not mean that it can replace Javascript. But it provides more convenience and flexibility to the Python Developers, especially Machine Learning Engineers. It provides flexibility to the developers.

Image creation: A Docker image is created that matches the Python environment specified by the Azure ML environment. The image is uploaded to the workspace. Image creation and uploading takes about five minutes. This stage happens once for each Python environment because the container is cached for subsequent runs. During image creation, logs are streamed to the run history.

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others. SpeechBrain is currently in beta. The recipes released with speechbrain implement speech processing systems with competitive or state-of-the-art performance. For more details, take a look into the corresponding implementation in recipes/dataset/.

The Shell is an interpreter that presents the command-line interface to users and allows them to interact with the kernel. It lets them control the system using commands entered from a keyboard. The Interpreter is a program that reads through instructions that are written in human readable programming languages and executes the instructions from top to bottom. It translates each instruction to a machine language the hardware can understand, executes it, and proceeds to the next instruction. The Command-Line Interface (CLI) is a program that accepts text input from the user to run commands on the operating system. It lets them configure the system, install software, and access features that aren't available in the graphical user interface.

How one gets educated for AI continues to be an area worth exploring with many options available. Charting one's career as a member of a newly-formed team working to leverage AI to help the business is best met with creativity and patience. It's as much a mission to find out how organizations are setting up for AI development as it is about finding out what you really want to do. The experience of one now-veteran machine modeler could be timely guidance for many in this context. Daniel Bourke is an entrepreneur running a YouTube site and writing about technology.

Hopsworks 1.x series brings many new features and improvements, ranging from services such as the Feature Store and Experiments, to enhanced support for distributed stream processing and analytics with Apache Flink and Apache Beam, to building Deep Learning pipelines with TensorFlow Extended (TFX), to code versioning support for Jupyter notebooks with Git, to all-new provenance/lineage of data across all steps of a data engineering and data science. We are also excited that Hopsworks 1.x is the back-bone of the all new Managed Hopsworks platform for AWS, Hopsworks.ai Hopsworks 1.x brings significant Feature Store improvements ranging from updated UI components to connectivity with external systems and feature discovery. Users of Hopsworks Enterprise can now easily connect to the Feature Store from their Databricks notebooks and Amazon Sagemaker. Documentation for connecting with these two platforms can be found at hopsworks.readthedocs.io

Setting up your Python environment for Machine Learning can be a tricky task. If you've never set up something like that before, you might spend hours fiddling with different commands trying to get the thing to work. But we just want to get right to the ML! In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You'll be able to get right down into the ML and never have to worry about installing packages ever again.

Python has become one of the most popular languages for deep learning, but setting it up is a challenging task, especially if you are developing or using applications with different python or package version requirements.