Plainsight Pipelines

Build vision AI solutions that help your business see more, know more, and solve more.

Centralized Video Analytics

Fully integrated tools and complete vision AI workflows facilitate dataset creation, model training, and production deployments to reveal valuable insights within your visual data.

Model Deployment Pipeline

Our intuitive model deployment pipeline tools streamline the creation and management of vision AI models in production environments. They make common pre- and post-processing augmentations like privacy masking, cropping, and resizing simple and easy to implement, allowing for continuous model refinement and faster time-to-value.

Custom Label Schemas for computer vision

Pipeline Configuration

Configure, manage, and deploy vision AI pipelines and automate complex workflows with a single interface.

Pipeline Blocks

Apply pre-configured data processors like resize, crop, and tiling that enhance accuracy and increase cost savings.

Model Performance Tracking

Monitor the status, performance, inferences, and activity of deployed custom models.

Batch & Streaming Deployments

Choose the right deployment type to optimize for cost efficiencies (batch) or latency (streaming).

Data Collection & Processing

Easy-to-use data collection and processing tools streamline and enhance this foundational stage of computer vision. Data assets are organized for ease of access, making it quick and easy to take your project to the next stage.

Continuously Collect Images & Video

Synchronize and organize visual data from cloud sources, mobile devices, and edge nodes.

Data Pre-Processing

Automatically process incoming data for model training and inferencing.

Image Tiling

Break high resolution assets into smaller tiles to increase the accuracy of inferences.

Dataset Version Control

Maintain compliance and traceability while making sure you always know which datasets were used to train which models.

Get started with Pipelines

Step 1: Sign-Up for Plainsight and access the Pipeline Manager

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Step 2: Select the Type of Pipeline You Want to Run and Give it a Name

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Step 3: Connect an Input to your Pipeline

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Step 4: Select the Model and Transformation Blocks 

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Step 5: Apply Transformation Blocks

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Step 6: Define Your Outputs and Publish Your Pipeline

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