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Required Tools
1.
MLOps & AI Overview
1.1.
Introduction
1.2.
AI then and now
1.2.1.
Expert Systems
1.2.2.
Fuzzy Logic
1.2.3.
Machine Learning
1.2.4.
Generative AI
1.2.5.
Reinforcement Learning
1.2.6.
Agentic AI
1.2.7.
MLOps
1.2.8.
Differences
1.3.
Examples
1.4.
Job Opportunities
1.5.
Terms to Know
1.6.
Model vs Library vs Framework
1.6.1.
Explanation
1.7.
Statistical vs ML Models
1.8.
Types of ML Models
1.9.
ML Lifecycle
1.9.1.
Data Preparation
1.9.2.
Data Cleaning
1.9.3.
Data Imputation
1.9.4.
Data Encoding
1.9.5.
Feature Engineering
1.9.5.1.
Vectors
1.9.5.2.
Embeddings
1.10.
Life Before MLOps
1.11.
Quiz
2.
Developer Tools
2.1.
Introduction
2.2.
UV
2.3.
Other Python Tools
2.4.
Error Handling
2.5.
Unit Test
2.6.
DuckDB
2.7.
JQ
2.8.
SQLite
3.
MLflow Introduction
3.1.
MLflow Experiment Structure
3.2.
MLflow Features
3.3.
YAML
4.
Cloud
4.1.
Overview
4.2.
Types
4.3.
Challenges
4.4.
AWS
4.4.1.
AWS Global Infra
4.4.2.
CIDR
4.4.3.
EC2
4.4.4.
S3
4.4.5.
IAM
4.4.6.
CloudShell
4.5.
Terraform
5.
MLflow Model Lifecycle
5.1.
Decorator
5.2.
HTTP Basics
5.3.
pydantic
5.4.
Model Flavors
5.5.
Model Serving
5.6.
Model Serving Types
5.7.
Auto ML
5.8.
CPU vs GPU
6.
Tools
6.1.
Containers
6.1.1.
VMs or Containers
6.1.2.
What container does
6.1.3.
Container Examples
7.
Productionizing ML Models
7.1.
Observability
7.2.
Drift
7.3.
Security
7.4.
Validation Frameworks
7.5.
Model Compression
7.6.
Ollama
7.7.
Best Practices
7.8.
SAAS Tools
8.
Good Reads
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MLOps and AI
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