Introduction to MLOps
- Course Introduction
- Getting Started with Machine Learning Team
- Introducing MLOps Engineer
- DevOps and MLOps A Comparison
- MLOps LifeCycle
- Continuous Integration CI Continuous Deployment CD Continuous Training CT Continuous Monitoring CM
- Finding and Exploring Right Tools from DevOps for MLOps
- MLOps Architecture
Data Collection and Preparation
- Data Collection and Preparation
- Data Ingestion ETL
- Idea of Data Lake
- Data Cleaning and Data Transformation
- Small to Medium Datasets Data Transformation Pandas Polars
- Demo Small to Medium Datasets Data Transformation Pandas Polars
- Large Datasets Apache Spark PySpark Dask
- Streaming Datasets Apache Kafka Apache Flink
- Demo Stream Data using Apache Kafka
- What is Feature Store
- Data Pipeline Orchestration Airflow Perfect
- Demo Data Pipeline Orchestration
Model Development and Training
- Model Development
- Model Training and Hyperparameter tuning
- World of CPUs and GPUs
- Introduction to MLflow
- Demo Setting up MLflow
- Demo Running and experiment and storing the result on MLflow
- Demo MLflow Model Artifact and Versioning
Model Deployment and Serving
- Model Serving
- Model Drift and OnlineOffline Serving
- Model Deployment and Serving
- Demo Model Serving using BentoML
- Demo Upgrading Model Versions with BentoML Serving
- Monitoring Tools PrometheusGrafanaEvidently
Automating Insurance Claim Reviews with MLflow and BentoML
- Deploy App for Insurance Agents to Upload all Insurance Claims
- Demo Generate Dummy Data for the Project
- Demo Setup MLflow server and run the ML Experiment
- Demo Register the Model and Setup BentoML for Serving ML Models
- Demo Upgrade Python Flask App to Connect to BentoML for Online Serving
Data Security and Governance
- Data Privacy and Data Security
- Data Access Management
- Data Retention
- Need of Compliance and GDPR
- HIPAA Compliance
- PCI Compliance
- Compliance Consequences and Penalties
- Compliance Summary