MLOps

Machine Learning Operations

Model creation must be

• scalable
• collaborative
• reproduceble

DataOps

Set of rules that ensure a high quality of data to train models

MLOps addresses to

• Versioning
• Model Tracking
• Feature Generation

MLOps (ML + Dev + Ops)

• ML (Experiment)

  • Data Acquisition
  • Business Undertanding
  • Initial Modeling

• Develop

  • Modeling + Testing
  • Continuous Integration
  • Continuous Deployment

• Operate

  • Continuous Delivery
  • Data Feedback Loop
  • System + Model Monitoring
  • Continuous Training

MLOps Process

Use Case Discorevy

• Business Understanding
• Use Case Identification
• Data Understanding
• Feasibility Study

Data Engineering

• Data Preparation

ML Pipeline

• Learning Algorithms
• Model Building/Training
• Model Experimentation
• Model Evaluation
• Model Serving

Production Deployment

• Deploy
• Automate

Production Monitoring

• Operate
• Monitor
• Optimize

MLOps Parts CRISP-ML

Part	Objective	Software
Feature Store
Data Versioning
Metadata Store
Model Versioning
Model Registration
Model Serving
Model Monitoring
Recycling of models
CI/CD

MLOps Stages

Stage	Definition
Stage 1	Model and Data Version Control
Stage 2	AutoML + Model and Data Version Control
Stage 3	AutoML + Model and Data Version Control + Model Serving
Stage 4	AutoML + Model and Data Version Control + Model Serving + Monitoring, Governance and Retraining (Fig 2)

Fig 2 - Continuous Training

Installing

Tools and Libraries

• Libraries
• Jupyter Notebook
• Docker

Tips

Jupyter Notebook environment (Conda)

    $ conda install -n [env] ipykernel
    $ python -m ipykernel install --user --name [env] --display-name "Python (mlops)"

References

• Landscape AI Infrastructure
• Jupyter Notebook Template