1. Bölüm: Introduction to MLOps, ML Engineering and Common Challenges in Machine Learning

• MLOps Maturity Levels
• Roles (ML Engineer, Data Scientist, Data Engineer)
• ML engineering in the real world
• What does an ML solution look like?
• High-level ML system design

2. Bölüm: The Machine Learning Development Process

• Model development environments
• Model development tools
• Model deployment methods
• Prepare environment (VM, Docker, AWS, AWS CLI, etc.)
• Create IAM role/user
• Create an S3 bucket, read and write data with Python/Boto3
• S3 compatible object storage: MinIO
• How MLflow helps model development

3. Bölüm: Developing an API with FastAPI

• Introduction to FastAPI
• Path parameters
• Query parameters
• Response model
• SQLModel
• Data validation with Pydantic
• Database CRUD Operations with FastAPI

4. Bölüm: Packaging Up The Codes

• Developing user-defined libraries and packages
• Testing, logging, and error handling

5. Bölüm: ML Model Deployment with FastAPI

• How to store and load models
• Loading models from MLflow
• Writing prediction results in database
• Detecting model/data drifts
• How to make ML models Docker images
• Deployment on Docker and Kubernetes
• Deployment on AWS EC2 Instance
• ML Pipelines

6.Bölüm: Packaging Up The Codes

• Developing user-defined libraries and packages
• Testing, logging, and error handling

7.Bölüm: How to Scale

• Apache Spark
• Ray

8.Bölüm: How to Automate

• CI/CD, Jenkins, and Ansible
• Terraform as IaC
• Data Science on AWS