Adopting ChatGPT
Adopting ChatGPT
Adopting ChatGPT
Notes from DataCamp's Understanding Machine Learning Course
Automated Testing
Automated Experiment Tracking
Automation
Automation Levels
Building the API
Adopting ChatGPT
CI/CD/CT/CM
CICD Pipelines
Notes from DataCamp's Understanding Machine Learning Course
Data Preparation
Data Quality and Ingestion
Data Versioning
Notes from DataCamp's Understanding Machine Learning Course
Preparing the model for deployment
Deployment Strategies
Deployment-Driven
Design Patterns
Design Phase
Writing effective ML documentation
Adopting ChatGPT
Experiment Tracking
Exploratory Data Analysis
Feature Engineering
Feature Engineering
Feature Engineering
Feature Store
Feature Stores
Hyperparameter Tuning
Notes from DataCamp's Understanding Machine Learning Course
Introduction
Introduction
Notes from DataCamp's Understanding Machine Learning Course
Logging Experiments in MLFlow
Notes from DataCamp's Understanding Machine Learning Course
Writing Maintenable ML Code
Metadata Store
ML Lifecycle
ML Model Lifecycle
MLOps Components
MLOps Lifecycle
MLOps Maturity
Model Build Pipelines
Notes from DataCamp's Understanding Machine Learning Course
Model Evaluation and Visualization
Model Governance
Adopting ChatGPT
Model Maintainance
Model Packaging
Model Registry
Model Reliability
Model Training
Monitoring
Monitoring Models
Notes from DataCamp's Understanding Machine Learning Course
Orchestration
Adopting ChatGPT
Packaging Machine Learning Models
Profiling and Versioning
Adopting ChatGPT
Reference Architecture
Designing reproducible experiments
Retraining the Model
Scalability
Serving Modes
Notes from DataCamp's Understanding Machine Learning Course
Testing
Testing Data
Testing Models
The MLOps Mindset
Adopting ChatGPT
Notes from DataCamp's Understanding Machine Learning Course
Unit Tests
Notes from DataCamp's Understanding Machine Learning Course
Adopting ChatGPT