Flex and Focus

ML data labeling solutions designed for ML experimentation and production

Alegion offers two different solutions

Alegion Flex

  • Small Datasets
  • Multiple Workflows
  • Fast Results
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Alegion Focus

  • Large Scale
  • Continuous Training
  • Full Integration
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During the experimentation phase, data science teams are focused on the testing of hypotheses, often running in parallel, with smaller amounts of data. Quality training data is of the highest importance as models are validated or discarded based on the results. To support these efforts, teams need multiple data labeling workflows, faster time to results, and greater flexibility to allow for experimentation variables. Once proven, it is only the successful hypotheses that then move into the focus production phase where the scaled labeled data is required to drive the model to it's peak precision.

That is why Alegion offers two different platform solutions: 

Alegion Flex designed for teams with a higher number of use cases, and lower volumes of training data 

Alegion Focus designed for high-volume data labeling at scale for production use cases
Alegion Focus - Flex Graphic-1

Alegion Flex

Rapid iteration, validation and quicker time to experimentation results. Designed for teams with a higher number of use cases, more variation in data labeling requirements across different iterations, and lower volumes of training data.

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Quickly power multiple proof of concept and pilot projects through rapid iteration

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Greater control control and predictability over data labeling costs, variables, and volume

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Accelerate experimentation with unmatched flexibility for computer vision and NLP use cases

Values/Benefits

Faster Experimentation

Shrink time to insight through fast end-to-end projects

  • Faster "time to start", faster iteration, faster to results
  • Optimized for iteration
  • Monitor progress and review annotations in the Data Science Portal
  • Run multiple experiments in parallel

Unmatched Flexibility

Mix and match data labels across different business needs

  • Optimize for multiple use cases and across specializations like NLP and Computer Vision
  • End-to-end project management by an expert Customer Success Manager
  • Offload hands-on labeling, tooling, workflow, workforce, and QA management entirely
  • Change labeling requirements without having to constantly negotiate contract terms
  • Easily match the amount of training data labels to each experiment

Greater Predictability

Budget beyond the horizon with confidence and control

  • Plan for multiple experiments without needing a crystal ball
  • Guaranteed SLAs to get projects started and quality results delivered
  • Use across teams and departments without new contracts and agreements
  • Minimize procurement impacts on your project schedules

Easily Transition to Production

Get to production fast with production quality and scale

  • When you're ready to operationalize a project, experiments can be easily transitioned to an Alegion Focus subscription
  • Customized production solutions provide for massive scale and support for operational requirements

How Flex Works

flex

Alegion Focus

Go to production with data labels optimized for scaling training data and model validation through the Alegion platform for a given use case.

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Improve model accuracy through high-volume training data throughput

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Expand training data coverage to operational conditions by addressing classification distribution, edge cases, and exceptions

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Optimize quality and accuracy while reducing the cost of human labeling over time

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Scales to include model validation and exception handling

Values/Benefits

Efficiency at scale

Reduce labeling costs and improve model accuracy and coverage with high-volume labeling

  • Increased volume, accuracy, and throughput with optimized workflows
  • ML-assisted tools accelerate labeling throughput
  • Minimize labeling impact on internal teams

Unmatched quality

Develop high-confidence models on high-quality, high-volume data

  • Confidence-driven QC workflows ensure quality at scale
  • Monitor progress and review annotations via the Data Science Portal
  • Quality KPI accountability, backed by an experienced CS team

Seamlessly Incorporate Training and Validation

Operationalize models and monitor production performance

  • Fine-tune models demanding high accuracy and precision
  • Compare model inferences with human evaluations
  • API integration for continuous training, validation, and exception-handling 

How Focus Works

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Additional Resources

Data Labeling for AI & ML Experimentation

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SmartPoly - Empowering Annotators with ML

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How ML Improves Quality

Balancing ML innovations and human expertise to deliver the highest quality

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