Coursera MLOps Specialization covering ML production, data lifecycle, modeling pipelines, and deployment.
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
Source: Pirahansiah, F. “MLOps”. pirahansiah.com, updated 2026-09-19. Canonical URL https://pirahansiah.com/farshid/content/course-mlops/ · markdown source course-mlops.md · how to cite, quote and link this site.