L06: MLOps
L06: MLOps
16:53
What is MLOps?
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to manage the entire ML lifecycle in a production environment.
In simple terms:
MLOps helps take ML models from notebooks to real-world applications reliably and at scale.
Key Components of MLOps
1. Version Control
Code versioning (Git)
Data versioning (DVC)
Model versioning (MLflow, SageMaker Model Registry)
2. CI/CD for ML
Automated training
Automated testing
Automated deployment
Continuous integration of new data and models
3. Model Deployment
Real-time endpoints
Batch inference
Serverless deployment
4. Monitoring & Observability
Performance monitoring
Data drift detection
Bias detection
Logging and alerting
5. Automation Pipelines
Training pipelines
Evaluation pipelines
Retraining pipelines
Benefits of MLOps
Faster model delivery to production
Reproducible experiments
Scalable model serving
Reduced operational risks
Continuous model improvement
Real-World Use Cases
Recommendation systems
Fraud detection platforms
Autonomous vehicles
Chatbots and GenAI systems
Healthcare diagnostics
MLOps in Cloud (AWS Example)
Amazon SageMaker for training & hosting
AWS CodePipeline for CI/CD
Amazon CloudWatch for monitoring
Amazon S3 for data storage
Amazon ECR for container images
Quiz
Q1. What is the main goal of MLOps?
A. To write ML algorithms
B. To manage ML models in production reliably
C. To design user interfaces
D. To collect only raw data
Correct Answer: B
Explanation: MLOps focuses on deploying, monitoring, automating, and maintaining ML models in production systems.
Q2. Which tool is commonly used for model versioning in MLOps?
A. Excel
B. GitHub
C. MLflow
D. PowerPoint
Correct Answer: C
Explanation: MLflow is widely used for tracking experiments, versioning models, and managing ML lifecycle artifacts in MLOps workflows.