MLOps - Automated Machine Learning Made Easy

Overview

Explore AI and ML optimization techniques to enhance business outcomes through data understanding and model improvement.

Syllabus

• Introduction to MLOps
Overview of MLOps and its importance
Key differences between MLOps and traditional DevOps
Business benefits of implementing MLOps
• Fundamentals of Machine Learning
Supervised vs. Unsupervised Learning
Overview of Popular Machine Learning Models
Introduction to Model Evaluation Metrics
• Data Understanding and Preprocessing
Data Collection and Exploration
Data Cleaning and Transformation
Feature Selection and Engineering
• Model Selection and Training
Choosing the Right Model for the Problem
Hyperparameter Tuning Techniques
Training Robust ML Models
• Model Evaluation and Validation
Cross-Validation Techniques
Bias-Variance Tradeoff
Performance Metrics Analysis
• Deploying Machine Learning Models
Model Packaging with Docker and Kubernetes
Continuous Integration/Continuous Deployment (CI/CD) for ML Models
Scaling Solutions for Production Environments
• Monitoring and Maintenance
Monitoring Model Performance in Production
Handling Concept Drift and Data Drift
Retraining and Updating Models
• Automation in MLOps
Automation of ML Pipelines with Tools (e.g., MLflow, Kubeflow)
Automated Testing and Validation of Models
Leveraging Infrastructure as Code (IaC) for ML
• Case Studies and Real-World Applications
MLOps in Retail, Finance, Healthcare
Case Study: End-to-End MLOps Pipeline
Lessons Learned from MLOps Implementations
• Emerging Trends in MLOps
Explainability and Ethics in AI
Privacy-Preserving Machine Learning
Future Directions in MLOps
• Course Review and Capstone Project
Course Summation and Review
Capstone Project: Building and Deploying an Automated ML Solution
Feedback and Course Wrap-up

Subjects

Conference Talks