Dataset task manager for machine learning

When training datasets for machine learning use cases, it’s essential to stay organized. When building a new model from a dataset, it’s helpful to know what tasks are being processed, their status, and when they started. Doing so allows you to keep track of machine learning model progress and ensure the entire dataset is processed.

With Retool, you can build a task manager for machine learning use cases. You’ll be able to track the progression of datasets, create new tasks, and add context for others — all within a single application.

Dataset task manager for machine learning

Build from a handful of drag-and-drop components

[object Object]
Table display details about datasets like their status, notes, start times, and ids in a table.
[object Object]
Button take action on datasets with buttons for creating, refreshing, clearing, and ending data processing tasks.
[object Object]
Dropdown add dropdown buttons so that team members can select and process datasets.

To manage your machine learning datasets, you’ll need to connect to a data store where datasets reside. With Retool, you can connect to dozens of data stores out of the box.

Step 1 Create resource1. Create resource
Step 2 Read data2. Read data
Step 3 Connect data with UI3. Connect data with UI
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Pedro Franceschi

Co-founder and CTO at Brex

Retool is incredible. It's been a critical for our ops from the start, and is the reason we’re able to scale so quickly. And the on-prem version with access controls & audit logs makes it easy to meet our compliance requirements.

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