1. Machine Learning: Background

• Machine Learning in Real Life: Models, Use Cases & Operations
• ML Models: Challenges & Solutions
• ML in Python: Top 5 Libraries
• What Is Machine Learning Engineering?
• Feature Importance: A Practical Guide

2. MLOps

• Ultimate Guide to MLOps: Process, Maturity Path and Best Practices
• MLOps Platforms: Benefits & Top Solutions
• What Is an MLOps Engineer?
• Implementing MLOps with Azure

3. Measuring AI performance

• ML Optimization: Essential Techniques
• Model Drift: What is it and ways to prevent It
• Data Drift: Types and detection methods
• ML Observability vs ML Monitoring

4. Model monitoring

• ML Model Monitoring: Practical guide to boosting model performance
• Monitoring ML Models in Production
• Amazon SageMaker Model Monitor
• Monitor for Bias & Fairness
• Credit Risk models monitoring

5. Explainable AI

• Explainable AI: How it Works and Why You Can’t Do AI Without It

6. Real-world Applications and Use Cases

• Machine Learning for business use cases
• Customer Lifetime Value (LTV)
• Lead/Demand Forecasting
• Credit Risk
• Dynamic Pricing
• Recommender systems (Recsys)
• Natural Language Processing (NLP)