Automate Data Labeling at Scale with Human-in-the-Loop

This blog post focuses on new features and improvements. For a comprehensive list, including bug fixes, please see the release notes.

Automate Data Labeling at Scale with Human-in-the-Loop

High-quality training data is the backbone of any AI model. However, labeling large datasets can be time-consuming and resource-intensive. Clarifai makes this seamless with Labeling Tasks, allowing you to automate data labeling at scale while keeping humans in the loop for accuracy and oversight.  

With Auto Annotation, you can instantly generate labels using AI models, significantly reducing the manual effort required. The reviewing capabilities ensure that human annotators can validate and correct AI-generated labels, improving overall dataset quality. Everything is managed within the Clarifai Platform, which offers a centralized workspace for teams to collaborate, monitor progress, and optimize workflows.

With our upgraded Labeling Tasks UI, you can create, assign, and review annotation tasks effortlessly. Whether you choose manual labeling for precision, AI-assisted labeling for efficiency, or a hybrid approach, our platform streamlines the process. Simply select a dataset, define your task, and let AI accelerate your workflow while human reviewers ensure quality.

The new UI enhances collaboration, enabling you to assign tasks to teams, integrate AI models, and monitor progress in real time. With flexible review settings and advanced prioritization, you stay in control of the entire annotation pipeline.

Start labeling smarter with Clarifai’s new and improved Labeling Tasks tool, now in Public Preview. If you are interested to explore and try out. Contact us get the access.

Control Center Updates

• We have updated access privileges for different roles:
  • Organization Contributors can now access the Overview tab pages, Usage & Operations tab pages, and detailed report pages of their charts.
  • Organization Users can now access the Overview tab pages, Usage & Operations tab pages, and detailed report pages of their charts.
  • Financial Managers can now access the Overview tab pages, Usage & Operations tab pages, Costs & Budget tab pages, and detailed report pages of their charts.

  

• We have added an empty state for the Overview tab. If all data is hidden, empty visuals will be shown, and no charts will be displayed.
• We now display two decimal places for financial values. For example, $20.1 is shown as $20.10.
• We have fixed an issue with table sorting where the number 0 was not being handled correctly. Now, numerical sorting works as expected, ensuring that 0 is properly ordered along with other values.
• We have added cross-navigation links between the detailed report charts in the Usage & Operations tab and the Costs & Budget tab. These links allow users to seamlessly access related charts. For example, the detailed report page for the Total Number of Operations chart now includes a link in the lower right corner, directing users to the Cost of Operations chart.

Improvements to the Data Utils library

We have open-sourced the Data Utils library to simplify multimedia data management and processing. In this release, we have introduced several updates:

• Added support for DOCX and Markdown file formats
• Enabled batch prediction for the ImageSummarizer pipeline

Python SDK Improvements

We have made several updates to the Python SDK to enhance functionality and performance.

• Added support for local dev runners from the CLI.
• Used the non-runtime path for tests.
• Fixed local tests.
• Caught additional codes that models have at startup.
• Introduced three instances when checkpoints can be downloaded.

Find all the updates here.

Additional changes

• Improved the navigation bar: We have made further adjustments to the navigation bar and links for improved usability and accessibility.

Ready to start building?