# MLOps
## Production ML
- Lifecycle overview
- Deployment
## Data Lifecycle
- Labeling, feature engineering
- Data storage
## Modeling Pipelines
- NAS, quantization
- Distillation, interpretability
## Deployment
- TF Serving, TorchServe
- Monitoring
MLOps — Machine Learning Engineering for Production
Coursera: Machine Learning Engineering for Production (MLOps) Specialization
Course 1: Introduction to Machine Learning in Production
- Week 1: Overview of the ML Lifecycle and Deployment
- Week 2: Selecting and Training a Model
- Week 3: Data Definition and Baseline
Course 2: Machine Learning Data Lifecycle in Production
- Week 1: Collecting, Labeling, and Validating data
- Week 2: Feature Engineering, Transformation, and Selection
- Week 3: Data Journey and Data Storage
- Week 4: Advanced Data Labeling Methods, Data Augmentation
Course 3: Machine Learning Modeling Pipelines in Production
- Week 1: Neural Architecture Search (NAS, AutoML)
- Week 2: Model Resource Management (PCA, SVD, quantization, pruning)
- Week 3: High-Performance Modeling (distributed training, knowledge distillation)
- Week 4: Model Analysis (TFMA, TFX)
- Week 5: Interpretability (SHAP, LIME, PDP)
Course 4: Deploying Machine Learning Models in Production
- Week 1-2: Model Serving (TensorFlow Serving, TorchServe, Triton)
- Week 3: Model Management and Delivery
- Week 4: Model Monitoring and Logging
#MLOps #ComputerVision #Tiziran