Machine Learning in Production Course Syllabus

This course provides a comprehensive introduction to deploying machine learning models in production environments, guiding learners through the full lifecycle of an ML project. You'll gain hands-on experience in designing scalable ML systems, addressing real-world challenges like concept drift, and implementing robust data pipelines. With approximately 15 hours of total content, the course is designed for flexible, self-paced learning, ideal for professionals aiming to bridge the gap between ML theory and practical deployment.

Module 1: Overview of the ML Lifecycle and Deployment

Estimated time: 3 hours

• Introduction to ML production systems
• Understanding deployment requirements and challenges
• Common deployment patterns for ML models
• Monitoring and maintaining ML systems in production

Module 2: Modeling Challenges and Strategies

Estimated time: 4 hours

• Model selection and strategy design
• Error analysis for improving model performance
• Handling different data types and formats
• Addressing class imbalance and skewed datasets

Module 3: Data Definition and Baseline

Estimated time: 4 hours

• Working with diverse data types
• Ensuring label consistency and quality
• Establishing model performance baselines
• Strategies for iterative improvement

Module 4: Data Pipelines and Feature Engineering

Estimated time: 4 hours

• Building data pipelines: gathering and cleaning data
• Data validation techniques
• Feature engineering and transformation
• Feature selection using TensorFlow Extended (TFX)

Module 5: Production Best Practices and Continuous Improvement

Estimated time: 4 hours

• Implementing best practices in ML deployment
• Progressive delivery techniques for ML systems
• Handling concept drift in production
• Prototyping and iterating on ML applications

Module 6: Final Project

Estimated time: 5 hours

• Design an end-to-end ML production system
• Prototype a deployable ML application with TFX
• Document deployment constraints and improvement strategies

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning fundamentals
• Experience with data preprocessing and model evaluation

What You'll Be Able to Do After

• Design and implement an end-to-end ML production system
• Build and validate robust data pipelines for ML applications
• Apply feature engineering and selection techniques using TFX
• Deploy models with monitoring and maintenance strategies
• Continuously improve ML systems using real-world feedback and concept drift detection