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Introducing: Playment’s Workflow Builder for Simplified Data Labeling Pipelines

Merlin Peter
October 29, 2020

One of the main bottlenecks of data labeling is managing multiple data annotation projects at scale. With various data sources and data pipelines, ML teams are spending valuable time on project management with restrictive, subpar tools that slow down innovation. 

The hard way - For example, consider a use case that involves simple steps like image classification, object detection, image segregation for instances, and lastly, full-pixel segmentation. To implement this simple workflow, firstly, you will require a dedicated engineering team to build the workflow management infrastructure. You will also need efficient systems to generate tasks, logically allocate tasks to annotators, route outputs from one step to further steps, and tools for quality check at every step. This project will most probably involve two to three months of dedicated engineering effort from a team of at least four/five engineers. This project will also come with overhead maintenance costs. Any iterations will require significant engineering effort due to the complexities of the workflow systems. The building cost is not justifiable, and needless to say, the platform will not be scalable in the long run.  

The easy way - To ease the load of additional engineering efforts and costs, Playment has engineered a built-in no-code Workflow Builder in GT Studio to help ML teams organise, configure, and optimise their workflows seamlessly. We have observed hundreds of workflows and have automated standard flows where anyone and configure workflows in a few minutes. Our no-code platform eliminates the need for dedicated engineering effort and makes the process simpler and faster. 

Effective Pipeline Setup With Workflow Builder

The Workflow Builder helps ML teams play around with pre-configured labeling steps to build creative pipelines for their labeling requirements. Once you upload the data on GT Studio, you can use the Workflow Builder for all further actions like:

Sample Workflow

The platform allows teams to create complex, customised workflows for a wide range of labeling tasks. Here’s a quick overview of the different workflow steps you can use: 

Advanced Tooling For Workflow Customisation 

There’s no one-size-fits-all approach when it comes to innovative ML initiatives. That’s why GT Studio’s workflow interface has a wide variety of functions, offering maximum flexibility to accommodate nuanced workflow and configuration logic setups. Here are a few essential customisation functions that help teams tailor the platform for their unique requirements. 

Python Code Compatibility

The platform’s python environment allows you to write custom python scripts or functions using the Python Code Step. This function can help in multiple ways. Here are a few examples:

Python Code Step Sample

Easy Conditional Data Query

The Empty Step allows easy task route configuration for conditional data queries. ML engineers often manually curate data for their problem statements. Conditional data query through string equal routes helps them automate the process of data curation for further labeling. 

For example, to train a self-driving car to differentiate between sunny and rainy weather conditions, previously an engineer would manually curate training datasets with sunny and rainy weather scenarios and isolating all other datasets. 

With the Workflow Builder, the engineer can first create a classification task using the Python code step and using the string equal route configuration to curate rainy, sunny, and snowy scenarios seamlessly in half the time.

Conditional Data Query Sample

Pre-Annotated Data + Human In The Loop Functions

Consider this workflow example: Input Data → Pre-label high-precision classes → Manual Correction Maker Task → Python-Automated Validations → Manual Sample Quality Check → Output (refer image)

Sample Workflow With Python Code Step

You can upload pre-annotated data and create specific jobs for improving model accuracies and data performance. The Workflow Builder’s easy data visualisation and curation via conditional data query make the workflow setup process completely frictionless. You can pre-label high-precision classes using ML proposals and other automation features. 

Create manual correction tasks using the Maker Step to fine-tune data as per different use case requirements or create a second review task using the Python Code Step to further improve the accuracy rates. You could then enable python-automated validations and use the Manual Step to configure a QC task for the pre-annotated data to weave human and machine functions suitable for your modelling process.

The output that surpasses pre-defined accuracy thresholds can be immediately exported via the customer dashboard. The rest are looped back to previous steps to improve accuracy rates. 

Enriched Workflows For Better Model Performance  

Playment’s Workflow Builder provides unlimited possibilities and is the perfect addition to an ML engineer’s toolkit for accelerated innovation. With the Workflow Builder, you can:

We’re committed to accelerating the AI age by focusing our efforts on bringing data labeling closer to automation with advanced tech infrastructures that help companies scale their ML initiatives without any hassle. 
For more information, visit our website or signup for a live demo of our labeling platform.