MLOps vs DevOps

 DevOpsMLOps
Code Versioning
Compute Environment
Continuous Integration/Delivery
Production Monitoring
Data Provenance
Datasets
Models
Hyperparameters
Metrics
Workflows

Why MLOps?

MLOps provide a technological backbone for managing the Machine Learning Lifecycle through Automation & Scalability. Below are few process which MLOps solves:

How models are created, trained and approved

Where models are stored

How models are deployed

How models are evaluated and monitored in production

How models are either corrected or removed from the production environment to minimize risks

How these processes repeat and intertwine to enable a cyclical machine learning operations process

TelioLabs MLOps Capability

Design

  • Data Gathering
  • Data Analysis
  • ML Use Case Prioritization
  • Data Availability Check

Model Development

  • Data Engineering
  • ML Model Engineering
  • Testing & Validation

Operations

  • Model Deployment
  • Model Versioning
  • CI/CD Pipelines
  • Model Monitoring
  • Model re-training

TelioLabs MLOps Value Proposition

TelioLabs provides professional services to create, collaborate, deploy & to manage lifecycle of an AI application in production.

  1. Create a fully automated end-to-end data pipeline
  2. Implement Model Evaluation for a Project
  3. Build the Continuous Delivery capability for Machine Learning
  4. Build the Generic framework for Data Labelling, Versioning, Security and Access
  5. Building Testing Capabilities

TelioLabs ML Ops Project Approach

  1. Workshop to understand customer business objectives
  2. Translate business objectives to MLOps implementation roadmap
  3. Design and implementation/development
  4. Test the Solutions
  5.   Go Live
  6. Support and Managed Services
  7. Continuous Service improvement plan
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