Build a Machine Learning Platform (From Scratch)

PART 1: LAYING THE FOUNDATIONS

1. GETTING STARTED WITH MLOPS AND ML ENGINEERING

2. WHAT IS MLOPS?

3. BUILDING APPLICATIONS ON KUBERNETES

4. DESIGNING RELIABLE ML SYSTEMS

5. ORCHESTRATING ML PIPELINES

6. PRODUCTIONIZING ML MODELS

PART 2: DEVELOPING REAL-WORLD ML PIPELINES

7. DATA ANALYSIS & PREPARATION

8. MODEL TRAINING AND VALIDATION: PART 1

9. MODEL TRAINING AND VALIDATION: PART 2

10. MODEL INFERENCE AND SERVING

PART 3: CLOSING THE LOOP

11. MONITORING AND EXPLAINABILITY

APPENDICES

APPENDIX A: INSTALLATION AND SETUP

APPENDIX B: BASICS OF YAML

APPENDIX C: TABLE OF TOOLS

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• Engenharia do software
• Aprendizagem mecânica
• Cloud computing

machine learning platform; mlops; devops for ml; kubeflow; mlflow; bentoml; evidently; feast; production machine learning; deploy ml models; data pipelines; feature store; kubernetes machine learning; continuous integration ml; monitoring ml models, Raschka