Machine Learning in Production

Machine Learning in Production is the discipline of deploying, monitoring, and maintaining machine learning models in live, operational environments to serve real-world applications and users. Moving beyond the experimental phase of model development, this field addresses the practical engineering challenges of integrating models into software systems, ensuring they are scalable, reliable, and performant under real-world load. It involves establishing robust pipelines for continuous monitoring to detect issues like data drift and performance degradation, as well as automating the processes for retraining and redeploying models to ensure they deliver sustained and accurate value over time, a practice often referred to as MLOps (Machine Learning Operations).

1. Introduction to MLOps
   1. Defining MLOps
      1. Purpose and Scope of MLOps
      2. Key Stakeholders in MLOps
         1. Data Scientists
         2. ML Engineers
         3. DevOps Engineers
         4. Product Managers
         5. Business Stakeholders
   2. Contrasting ML Development and Traditional Software Development
      1. Differences in Lifecycle and Iteration
      2. Data Dependency and Data Management
      3. Model Lifecycle vs Software Lifecycle
      4. Testing and Validation Differences
      5. Experimental Nature of ML Development
   3. The MLOps Lifecycle
      1. Scoping and Design
         1. Identifying Business Needs
         2. Translating Business Needs to ML Tasks
         3. Problem Formulation
      2. Data Management
         1. Data Collection
         2. Data Cleaning and Preprocessing
         3. Data Storage and Access
         4. Data Governance
      3. Model Development
         1. Model Selection
         2. Model Training
         3. Model Evaluation
         4. Hyperparameter Tuning
      4. Deployment
         1. Model Packaging
         2. Model Integration with Applications
         3. Infrastructure Setup
      5. Monitoring and Maintenance
         1. Performance Tracking
         2. Model Updating and Retraining
         3. Incident Response
   4. Core Principles of MLOps
      1. Automation
         1. Automated Workflows
         2. Reducing Manual Intervention
         3. Pipeline Orchestration
      2. Reproducibility
         1. Ensuring Consistent Results
         2. Reproducible Environments
         3. Deterministic Processes
      3. Versioning
         1. Version Control for Code
         2. Version Control for Data and Models
         3. Artifact Management
      4. Collaboration
         1. Cross-functional Teamwork
         2. Communication and Documentation
         3. Knowledge Sharing
      5. Continuous Integration, Delivery, and Training
         1. Automated Testing and Validation
         2. Continuous Model Deployment
         3. Continuous Model Retraining
         4. Feedback Loops