Course Description

Welcome to MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services! This cutting-edge course is designed to tackle one of the most significant challenges in data science: successful deployment. With a focus on automating pipeline functions and continuously optimizing performance, this course will equip you with the essential skills to monitor, maintain, and improve your data pipelines for prediction.

In this intermediate-level course, you'll dive deep into the world of MLOps, learning how to set up automated monitoring systems, detect and address data drift and model drift, and implement effective feedback loops. You'll also explore the critical aspects of model stability, setting up triggers and alarms, and addressing ethical concerns in machine learning through the "Responsible Data Science" framework.

What students will learn from the course

• Master the art of automated monitoring for data pipelines
• Understand and mitigate data drift, model drift, and feedback loops
• Implement triggers and alarms for timely problem resolution
• Apply principles of Continuous Integration (CI), Continuous Delivery (CDE), and Continuous Deployment (CD)
• Navigate the differing requirements of model training versus model inference
• Address ethical issues in machine learning and implement responsible AI practices
• Gain hands-on experience with Amazon Web Services (AWS) for MLOps

Prerequisites or skills necessary to complete the course

• Have completed the prerequisite courses: "Predictive Analytics: Basic Modeling Techniques" and "MLOps1 (AWS): Deploying AI and ML Models in Production using Amazon Web Services"
• Be comfortable working with Python in a cloud-based environment
• Have some familiarity with software development concepts, including git, logging, testing, debugging, code optimization, and security

What the course will cover

• Automated monitoring of data pipelines for prediction
• Data drift, model drift, and feedback loops
• Model stability and performance optimization
• Triggers and alarms for pipeline management
• Ethical issues in machine learning and responsible AI
• Continuous Integration (CI), Continuous Delivery (CDE), and Continuous Deployment (CD)
• Model security and pipeline optimization
• Hands-on experience with Amazon Web Services (AWS)

Who this course is for

• Data scientists looking to enhance their deployment skills
• Machine learning engineers seeking to automate and optimize their pipelines
• Software developers interested in expanding into MLOps
• IT professionals wanting to specialize in AI/ML infrastructure
• Anyone looking to advance their career in the rapidly growing field of MLOps

How learners can use these skills in the real world

• Improving the success rate of data science projects in various industries
• Enhancing the reliability and performance of AI/ML models in production environments
• Implementing robust monitoring systems for early detection of issues in data pipelines
• Ensuring ethical and responsible use of AI in business applications
• Optimizing resource allocation and cost-efficiency in cloud-based ML operations
• Streamlining the collaboration between data scientists and IT operations teams

Syllabus

Week 1 – Drift and Feedback Loops

• Module 1: Training Versus Inference Pipelines
• Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

• Module 3: Triggers & Alarms
• Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

• Module 5: CI/CD

Week 4 – Model Security and Responsible AI

• Module 6: Responsible AI