Data science and engineering workflows often span multiple stages, from warehousing to orchestration, using tools like BigQuery, dbt, and Airbyte. This automation can improve the productivity of experts while democratizing access to large-scale data analysis. In this paper, we introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering workflows, featuring 494 real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks, derived from real-world use cases, evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems. To balance realistic simulation with evaluation simplicity, we devote significant effort to developing automatic configurations for task setup and carefully crafting evaluation metrics for each task.

A field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming. In this article, we're going to go over three aspects of machine learning pipeline design that tend to be tedious but nonetheless important. After that, we're going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science.

A field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming. In this article, we're going to go over three aspects of machine learning pipeline design that tend to be tedious but nonetheless important. After that, we're going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science.