Data Products
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Accelerate ROI of AI & Data Science Outcomes with Data Products

DATA SCIENCE OUTCOMES

Solutions often built as experiments don't routinely scale to broader audience & production

  • Analytics Stored in Notebooks
  • Lacking tools for scaling & integrating to business products
  • Lack of connection to business case

MATCH TO BUSINESS

  • Discovery Workshop
  • Data Assessment & Strategy
  • Business case identification for adaptive systems
  • ML metrics
  • Pipeline bucketing / segmentation
  • Identify tools for merging outcomes into software (e.g, libraries, SDKs, etc.)

DATA PRODUCT POC

  • Co-modeling & Validation
  • Establish lifecycle and cadence between Software Engineering & Data Science
  • Review, assess, repeat

OPERATIONALIZE

  • Scale life cycle, cadence and tooling to greater organization

How we help clients

Featured Insights for Data Products

Data Product Offerings

User Feedback: Ingest & Feature Engineering

  • VALUE PROP: Insight into customer use of systems
  • ROI: cost of building irrelevant features
  • Price and Deliverables
    $18,000 - discovery workshop and system design document containing feedback system implementation plans
    $45,000 - high fidelity wireframes describing user feedback collection patterns
    $45,000 - CLI for local feature engineering data transformation pipeline
    $96,000 - high fidelity wireframes describing user feedback collection patterns, CLI for local feature engineering data transformation pipeline

Machine Learning CI/CD Pipeline

  • VALUE PROP: costs less to deploy MLDS ownership over product
  • ROI: Percentage of staff time required for deploying ML ongoing percentage of customer adoption/continued use
  • Price and Deliverables
    $18,000 - discovery workshop and system design document containing ML CI/CD system implementation plans
    $96,000 - a suite of tools for automating data processing, deployment of model training on cloud managed-hardware services, serving of results, and auto-scaling of deployment

Model Interpretability System

  • VALUE PROP: Mitigate bias in ML systems
  • ROI: Cost of regulatory audit / Legal Action
  • Price and Deliverables
    $18,000 - discovery workshop and system design document containing interpretability system implementation plans
    $75,000 - CLI to output model interpretability metrics locally
    $128,000 - microservice serving interpretability metrics, a cloud-based relational database for storing interpretability metrics, and a dashboard for visualization of interpretability metrics

Data Model & Skew Monitoring

  • VALUE PROP: Ensure your product is serving customers relevant/accurate information
  • ROI: Percentage of customer revenue not converted on miss served information
  • Price and Deliverables
    $18,000 - discovery workshop and system design document containing skew system implementation plans
    $48,000 - CLI for calculating data and model skew locally
    $96,000 - microservice serving mini-batches of skew metrics and a cloud-based relational database storing the skew metrics

Custom Solutions

  • In addition to these offerings we routinely create custom solutions.
  • Talk with One of Our Experts >

Active Learning System

  • VALUE PROP: systems adapt to changing business conditions
  • ROI: cost of manually updating systems
  • Price and Deliverables
    $18,000 - discovery workshop and system design document containing active learning system implementation plans
    $60,000 - model and data versioning tools
    $100,000 - cloud-based managed-hardware service automated model retraining tools, integration of model and data skewness triggers for model retraining, automated redeployment tools for inference engine
    $160,000 - user feedback ingestion and feature engineering pipeline tools, cloud-based managed-hardware service automated model retraining tools, integration of model and data skewness triggers for model retraining, automated redeployment tools for inference engine, model and data versioning tools

Expero delivers data products like recommenders, predictors and risk scoring to accelerate productization of ML and adaptive analytics across the enterprise.

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