MLOps2 (Azure): Data Pipeline Automation & Optimization

Course Description

Welcome to MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning, an innovative and essential course designed to tackle one of the most significant challenges in data science projects - deployment. This intermediate-level course is part of the Professional Certificate in Machine Learning Operations with Microsoft Azure (MLOps with Azure) program, offering you a unique opportunity to master the art of automating and optimizing your data pipeline for enhanced model performance and stability.

What Students Will Learn

In this comprehensive course, you'll gain invaluable skills in setting up automated monitoring systems for your data pipeline, ensuring your predictions remain accurate and reliable. You'll dive deep into crucial concepts such as data drift, model drift, and feedback loops, learning how to identify and mitigate these issues that can impair model performance. The course also covers setting up triggers and alarms, allowing you to promptly address model instability problems.

Moreover, you'll explore the critical realm of ethical issues in machine learning, understanding the associated risks and learning about the "Responsible Data Science" framework. By the end of this course, you'll be equipped to:

• Differentiate and manage the requirements of model training versus model inference in your pipeline
• Implement checks for model drift, data drift, and feedback loops
• Apply principles of Continuous Integration (CI), Continuous Delivery (CDE), and Continuous Deployment (CD)
• Set up automated monitoring systems for your data pipeline
• Address ethical concerns in machine learning projects

Prerequisites

To get the most out of this course, participants should:

• Have completed the previous two courses in the series:
  • Predictive Analytics: Basic Modeling Techniques
  • MLOps 1 (Azure): Deploying AI and ML Models in Production using Microsoft Azure Machine Learning
• 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

Course Coverage

• Training versus Inference Pipelines
• Drift & Feedback Loops
• Triggers & Alarms
• Model Stability
• Continuous Integration & Continuous Deployment/Delivery (CI/CD)
• Model Security
• Responsible AI and ethical considerations in machine learning

Who This Course Is For

This course is ideal for data scientists, machine learning engineers, and AI professionals who want to enhance their skills in deploying and maintaining machine learning models in production environments. It's particularly suited for those looking to specialize in MLOps using Microsoft Azure Machine Learning and who want to ensure their models remain effective and ethical in real-world applications.

Real-World Application of Skills

The skills acquired in this course are directly applicable to real-world scenarios in various industries. Learners will be able to:

• Implement robust, automated data pipelines in production environments
• Ensure the long-term performance and reliability of deployed machine learning models
• Identify and address issues that could compromise model accuracy over time
• Apply ethical considerations to machine learning projects, promoting responsible AI practices
• Enhance the overall success rate of data science projects by mastering the deployment phase
• Contribute to the development of more stable, efficient, and ethical AI systems in their organizations

Syllabus

By enrolling in this course, you're taking a significant step towards becoming a more proficient and responsible data scientist, capable of deploying and maintaining high-performance machine learning models in real-world scenarios.