MLOps

Artificial Intelligence (AI) Strategy

Vidhi Chugh

AI strategist and ethicist

Operationalization

Bringing advanced AI models to business-as-usual operations.

Advanced AI models

Artificial Intelligence (AI) Strategy

Code and data version control

  • Experiments result in multiple code versions

Experiments result in multiple code versions

  • Maintained through version control systems
  • Data versions

Data versions

  • New data or new features
Artificial Intelligence (AI) Strategy

Portability

  • Environments of ML projects
    • Training
    • Validation
    • Production

 

  • Consistent throughout the lifecycle

    • Libraries
    • Frameworks
    • Versions

 

Libraries, frameworks, and versions

Artificial Intelligence (AI) Strategy

Right time to think!

  • Managing different versions and dependencies

Focus is on immediate business value

  • Focus is on immediate business value
  • Scalability and architecture as an afterthought

Scalability and architecture

  • Ad-hoc approaches create challenges
Artificial Intelligence (AI) Strategy

Automated data management

  • Effort to re-prepare training data
  • Errors, changes, or updates
  • Automated data management for quality
  • Quality checks and alerts

Errors, changes, or updates

Automated data management for quality

Quality checks and alerts

Artificial Intelligence (AI) Strategy

Need for automation

  • Manual interventions = delays
    • Degraded model performance
    • Error analysis
    • Corrective action

Manual interventions

Model performance

 

  • Considerable time passed
  • Incorrect predictions
  • Negative consequences
Artificial Intelligence (AI) Strategy

Code refactoring

 

Refactor the initial code

 

  • Lack of core engineering best practices

 

  • Need to refactor the initial code
    • Significant risk
    • Original logic misunderstood or misrepresented
Artificial Intelligence (AI) Strategy

Characteristics of efficient architecture

 

  • Reliable systems require effort
  • MLOps = standardizing processes and automated pipelines

 

  • Automation:
    • Reusable modules - data products, code
    • Automated testing frameworks
    • Reduces the risk of refactoring

 

Automation

Artificial Intelligence (AI) Strategy

Characteristics of efficient architecture

 

Docker bundles model with environment

 

  • Docker bundles model with environment

  • Fewer manual interventions = efficient lifecycle

  • Maintaining model performance is challenging

 

  • MLOps to monitor the models
    • 30% fewer model shelve
    • 60% increase in value
Artificial Intelligence (AI) Strategy

Let's practice!

Artificial Intelligence (AI) Strategy

Preparing Video For Download...