Mlflow 8. Models
MLOps
MLOps ; ML의 지속적 배포 & 자동화 파이프라인
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learnins
[Week 2-code] Feature Engineering with TFX
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 2-code 2] Feature Engineering Pipeline
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 2-code 1] Simple Feature Engineering
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 2-3] Feature Selection
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 2-2] Feature Transformation at Scale
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 2-1] Feature Engineering
(coursera) Machine Learning Data Lifecycle in Production - Feature Engineering, Transformation and Selection
[Week 1-code] TensorFlow Data Validation (TFDV)
(coursera) Machine Learning Data Lifecycle in Production - Collecting, Labeling and Validating Data
[kubeflow] 2.Kubeflow Components (2)
Katib, Pipeline, Training of ML models, Serving Models
[Week 1-4] Validating Data
(coursera) Machine Learning Data Lifecycle in Production - Collecting, Labeling and Validating Data
[kubeflow] 2.Kubeflow Components (1)
Dashboard, Notebook Servers, Fairing
[Week 1-3] Labeling Data
(coursera) Machine Learning Data Lifecycle in Production - Collecting, Labeling and Validating Data
[kubeflow] 1.Introduction to Kubeflow (2)
kubernetes 복습, kubeflow 설치
[Week 1-2] Collecting Data
(coursera) Machine Learning Data Lifecycle in Production - Collecting, Labeling and Validating Data
[kubeflow] 1.Introduction to Kubeflow (1)
ML Workflow, kubeflow
[Week 1-1] Introduction to Machine Learning Engineering in Production
(coursera) Machine Learning Data Lifecycle in Production - Collecting, Labeling, Validating Data
[Week 3-2] Label and Organize Data
(coursera) Introduction to ML in production - 3.Data Definition and Baseline
[Week 3-1] Define Data and Establish Baseline
(coursera) Introduction to ML in production - 3.Data Definition and Baseline
[Week 2-3] Data Iteration
(coursera) Introduction to ML in production - 2.Select and Train a Model
[Week 2-2] Error analysis and performance auditing
(coursera) Introduction to ML in production - 2.Select and Train a Model
[Week 2-1] Selecting and Training a Model
(coursera) Introduction to ML in production - 2.Select and Train a Model
[Week 1-b] Monitoring Machine Learning Models in Production
(coursera) Introduction to ML in production - 1.Overview of the ML Lifecycle and Deployment
[Week 1-a] Concept & Data Drift
(coursera) Introduction to ML in production - 1.Overview of the ML Lifecycle and Deployment
[Week 1-3] Deployment
(coursera) Introduction to ML in production - 1.Overview of the ML Lifecycle and Deployment
[Week 1-2] ML Project Life Cycle
(coursera) Introduction to ML in production - 1.Overview of the ML Lifecycle and Deployment
[Week 1-1] Introduction to MLOps
(coursera) Introduction to ML in production - 1.Overview of the ML Lifecycle and Deployment