Machine Learning Operations Solution

Machine Learning Operations Solution

Model Interpretability

Treasure Mapping

ML Operations

System Control

Model Development

Forecasting & Prediction

MLops as a Service
  1. Enable automated model training/serving
  2. Instantiate model deployment pipelines
  3. Plumb data pipelines to models and serving
  4. Serialization of data pipelines and models for hot swapping and reproducibility
  5. Install and plumb front end analytics platforms
How does it work?

Executive Summary

Productionizing machine learning solutions takes a new set of tools and skills unfamiliar to many devops specialists. Reproducibility of data pipelines and models is key to scaling and updating data science workflows in production. These considerations simply aren’t a factor in traditional CI/CD environments, but Expero’s machine learning ops (MLops) team is here to help! Additionally, data science expert users consist of both analysts and coders. Enabling dual workflows in an integrated development/analysis environment is essential to enterprise teams. Using the latest tools and techniques, Expero can build your production-ready data science environment from data prep, to model training and deployment environments, and data analysis dashboards.

Solution Modules

Engagements

Solution Engagements

2-5 Day Assessments

3-5 Week PoC

3-6 Month Project

Data assessment focused on business goals and user enablement.
Working data pipeline prototype for data/user subset, from experimentation to deployment.
Working data pipeline, scalable to your organization from experimentation to deployment.

Deliverables may include

  • Report on future state of data tooling and strategy for enabling future vision
  • Recommended tools for your business

Prerequisites requested

  • Description of data, platform users, stakeholders involved and implementation timeline

Customer team members requested

  • Project manager - business vision
  • Technical lead - technology goals
No items found.
MLops as a Service
  1. Enable automated model training/serving
  2. Instantiate model deployment pipelines
  3. Plumb data pipelines to models and serving
  4. Serialization of data pipelines and models for hot swapping and reproducibility
  5. Install and plumb front end analytics platforms
How does it work?
  • Data lake
  • DS sandboxes
  • Data pipeline versioning + persistence
  • Model training
  • Model versioning + persistence
  • Model inference + serving
  • Model staleness testing
  • Pipeline and model updating
  • User-in-the-loop model updating
  • Front end analytics and BI

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Included Technologies

Preferred Technologies

Why Our Solution is Different

MLops as a Service

  1. Enable automated model training/serving
  2. Instantiate model deployment pipelines
  3. Plumb data pipelines to models and serving
  4. Serialization of data pipelines and models for hot swapping and reproducibility
  5. Install and plumb front end analytics platforms

Solution Focus Areas

  • Data lake
  • DS sandboxes
  • Data pipeline versioning + persistence
  • Model training
  • Model versioning + persistence
  • Model inference + serving
  • Model staleness testing
  • Pipeline and model updating
  • User-in-the-loop model updating
  • Front end analytics and BI