L04: MLOps Roles & Responsibilities

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What is MLOps?

MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy, monitor, and manage ML models reliably in production.

In simple terms:

MLOps ensures that ML models work smoothly in real-world systems, not just in notebooks.

Why MLOps is Important

Bridges gap between research and production
Enables automation of training, testing, and deployment
Ensures model reliability, scalability, and security
Supports continuous monitoring and retraining
Handles data drift and performance degradation

Key MLOps Roles

1. Data Scientist

Builds and trains ML models
Performs feature engineering
Evaluates model performance
Experiments with algorithms

2. ML Engineer

Deploys models into production
Builds ML pipelines
Optimizes inference performance
Integrates models with applications

3. MLOps Engineer

Automates training and deployment
Manages CI/CD for ML
Monitors models and data drift
Handles versioning and rollback

4. Data Engineer

Builds data pipelines
Manages data storage and ETL
Ensures data quality and availability

5. Cloud / Platform Engineer

Manages cloud infrastructure
Handles scalability, security, and cost optimization
Supports tools like Kubernetes, Docker, SageMaker

6. Product Manager / Business Analyst

Defines use cases and KPIs
Aligns ML solutions with business goals
Tracks model impact

Responsibilities Across ML Lifecycle

PhaseResponsible RolesData CollectionData EngineerModel BuildingData ScientistModel DeploymentML EngineerAutomation & CI/CDMLOps EngineerMonitoring & RetrainingMLOps Engineer + Data ScientistInfrastructureCloud Engineer

Real-World Example

In an e-commerce recommendation system:

Data Scientist builds the recommendation model
ML Engineer deploys it as an API
MLOps Engineer sets up pipelines and monitoring
Data Engineer manages streaming user data
Product Manager tracks business metrics like CTR

Quiz

Q1. Who is mainly responsible for deploying ML models into production?

A. Data Scientist
B. Business Analyst
C. ML Engineer
D. UI Designer

Correct Answer: C
Explanation: ML Engineers focus on integrating trained models into production systems and ensuring they run efficiently and reliably.

Q2. Which role handles automation, CI/CD, and monitoring in ML systems?

A. Data Engineer
B. MLOps Engineer
C. Product Manager
D. Software Tester

Correct Answer: B
Explanation: MLOps Engineers manage automation, continuous integration, deployment pipelines, and model monitoring in production.