Key Takeaways

VESSL AI has launched a new feature, VESSL Pipeline, enabling users to easily define and manage complex machine learning workflows as pipelines. This feature unlocks reproducible and consistent AI workflows by utilizing directed acyclic graphs (DAGs), simplifying the way you handle intricate ML tasks. If you’re interested in orchestrating ML workflows, visit app.vessl.ai↗ today to enjoy $15 in credits. If you want to learn more about how it works, please refer to our article or see the official documentation↗.

Why Did We Build This?

Automating AI workflows is challenging. AI/ML projects inherently involve multiple stages, from data preprocessing to model deployment, each with its own complexities. Managing these stages manually is time-consuming and prone to errors. We’ve built the VESSL Pipeline to address the key challenges:

• Complexity of ML Workflows: ML workflows involve several dependent steps like data preprocessing, model training, evaluation, and deployment. Manually coordinating these steps can lead to inefficiencies and mistakes.
• Lack of Reproducibility: Sharing experimental results or repeating experiments often suffers due to inconsistent settings or environments, undermining the trustworthiness of the project outcomes.
• Limited Visibility: Users often struggle with a lack of transparency in their ML workflows. When a pipeline fails or produces unexpected results, pinpointing the exact issue can be challenging without proper tools.

There's a need for enhanced visibility into each step of the workflow to facilitate easier debugging and optimization. We created the VESSL Pipeline to address this problem and make managing complex workflows more straightforward and collaborative.

What Values Do We Offer?

VESSL Pipeline provides a range of features and benefits to help users define and manage complex machine learning workflows effortlessly:

• Pipeline configuration with YAML and DAG visualization: Create and modify pipelines using YAML files or through a user-friendly drag-and-drop UI that organizes steps into a directed acyclic graph (DAG). This feature presents even complex workflows in an easily understandable format, reducing the need for extensive coding.
• Reproducibility and collaboration: Pipeline configurations and execution histories are automatically saved, making it easy to share and collaborate with team members. This ensures experiments are reproducible and project consistency is maintained.
• Enhanced debugging: With detailed monitoring and logging for each step, users gain clear insights into their workflows. This transparency allows for quicker identification and resolution of issues, improving the efficiency of model development.

Walkthrough

Here's a brief guide on how to use VESSL Pipeline:

1. Access the Pipeline menu on VESSL:

• Log in to VESSL and navigate to the sidebar.
• Click on the Pipeline menu at the sidebar of the pipeline dashboard.

2. Create a new Pipeline:

• Click the Create New Pipeline button to start a new pipeline.

• Provide a name and description for your pipeline for easy identification.

3. Define pipeline steps and variables:

• Add the necessary steps to your pipeline, specifying the tasks to be executed in each step.
• Set up dependencies between steps to control the execution order.
• Define pipeline variables to manage values like data paths and environment variables.

4. Run and monitor your pipeline:

• Click the Run Pipeline button to execute your pipeline.

• Monitor the progress and status of your pipeline in real-time from the dashboard.

What’s Next?

VESSL AI is committed to continuously improving and expanding the Pipeline feature based on user feedback. For the future updates, we plan to introduce items below:

• Monitoring and notification features: Enhancing real-time monitoring and notification functionalities to enable swift responses to any issues during pipeline execution.
• Visibility and debugging tools: Providing more detailed logs, visualizations, and diagnostic tools to make debugging even more efficient.
• User Experience: Improving the UI/UX for a more intuitive interface and providing comprehensive educational materials and tutorials.

VESSL AI aims to set a new standard in machine learning workflow management. Your valuable feedback and participation are the driving forces behind the continuous evolution of VESSL. We look forward to your continued interest and support. If you have any feedback, please contact support@vessl.ai↗.

• Experience VESSL Pipeline↗ today.

Jay Chun, CTO

Wayne Kim, Technical Communicator