Building, Evaluating, and Operationalizing ML Models
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Building, Evaluating, and Operationalizing ML Models
This course is part of Azure ML Bootcamp: Machine Learning on the Cloud Specialization
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What you'll learn
Understand how to choose the right algorithms for regression and classification tasks.
Master the use of Azure ML Studio for building, customizing, and deploying ML models.
Gain expertise in performance evaluation using cross-validation and various metrics.
Learn to structure and automate ML workflows through efficient pipelines.
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There are 3 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive into the entire process of building, evaluating, and operationalizing machine learning (ML) models. Starting with data exploration, you'll learn how to select the right algorithms for regression and classification tasks and fine-tune them for optimal performance. As you progress, you'll gain hands-on experience with tools like Azure ML Studio, experimenting with model customization, feature engineering, and advanced algorithms such as XGBoost and Neural Networks. You'll also discover how to evaluate models, optimize performance, and deploy them effectively. The course offers practical demos, empowering you to implement everything from simple models to complex pipelines in ML workflows. Throughout the course, you'll explore model evaluation techniques, including cross-validation and performance metrics, and learn how to address issues like overfitting and model drift. You'll also engage with ML-Ops concepts, discovering how to structure scalable pipelines, automate workflows, and manage the lifecycle of your models. This course is perfect for those looking to gain real-world ML skills, especially those interested in Azure ML Studio and automated pipelines. Whether you’re starting with basic ML concepts or expanding your knowledge, this course provides a comprehensive guide. You’ll learn to make data-driven decisions while optimizing the end-to-end machine learning lifecycle. By the end, you'll be prepared to build and deploy models that are both efficient and scalable, while also staying on top of versioning and performance monitoring.
In this module, we will guide you through the process of building machine learning models using Azure ML Studio. You will explore various algorithms, including ensemble methods and advanced models, while learning how to select and fine-tune the best algorithm for your dataset. With practical demos, you’ll gain hands-on experience building, customizing, and optimizing models to meet your specific needs.
What's included
13 videos2 readings1 assignment
13 videos•Total 136 minutes
- DEMO - Loading a Dataset and Exploring Basic Statistics in Azure ML Studio•14 minutes
- Overview of Common Machine Learning Algorithms for Regression and Classification•9 minutes
- Selecting the Best Algorithm Based on Data Type and Problem Complexity•9 minutes
- Introduction to Ensemble Methods: Random Forests, Gradient Boosting Machines•7 minutes
- DEMO - Selecting an Appropriate Model for a Dataset in Azure ML Studio•13 minutes
- Step-by-Step Process for Building a Model Using Pre-Built Modules•9 minutes
- Customizing Models with Advanced Settings and Hyperparameters•6 minutes
- DEMO - Building a Classification Model Using Azure ML Studio•20 minutes
- Feature Engineering: Creating New Features to Improve Model Performance•7 minutes
- Handling Missing Data and Categorical Variables Using Preprocessing Techniques•7 minutes
- Using Cross-Validation to Assess Model Generalization•8 minutes
- Implementing Complex Algorithms: XGBoost, LightGBM, and Neural Networks•8 minutes
- DEMO - Building an Advanced Model with Feature Engineering in Azure ML Studio•20 minutes
2 readings•Total 20 minutes
- Introduction to the Course 'Building, Evaluating, and Operationalizing ML Models'•10 minutes
- Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
- Building Machine Learning Models - Assessment•15 minutes
In this module, we will dive into the evaluation and optimization of machine learning models. You will learn how to assess model performance using key metrics and cross-validation techniques, and explore methods for improving model efficiency. Additionally, you will understand the importance of tracking model drift and how to retrain models to keep them relevant and effective over time.
What's included
13 videos1 assignment
13 videos•Total 98 minutes
- Comparing Multiple Models to Select the Best Performer•8 minutes
- Evaluating Model Performance Using Cross-Validation and Validation Datasets•8 minutes
- Utilizing Model Performance Metrics•11 minutes
- Clustering Evaluation: Silhouette Score, Adjusted Rand Index•9 minutes
- DEMO - Performing Hyperparameter Tuning with Azure HyperDrive•12 minutes
- Regularization Techniques to Improve Model Performance•9 minutes
- Introduction to Model Ensembling•4 minutes
- Model Pruning and Simplification for Efficiency•4 minutes
- Optimizing Model Inference Speed and Reducing Latency•8 minutes
- DEMO - Running AutoML Experiment Using Regression•14 minutes
- Tracking Model Performance Over Time and Detecting Model Drift•3 minutes
- Techniques for Model Retraining and Versioning•4 minutes
- Best Practices for Managing Model Lifecycle and Deployment•4 minutes
1 assignment•Total 15 minutes
- Model Evaluation and Optimization - Assessment•15 minutes
In this module, we will explore the power of machine learning pipelines for automating and scaling ML workflows. You will learn to design, build, and manage reusable pipelines, and incorporate custom code to tailor your processes. By the end of this section, you'll be equipped to handle complex workflows and ensure robust pipeline performance through advanced management techniques and versioning practices.
What's included
15 videos1 reading3 assignments
15 videos•Total 85 minutes
- Overview of ML Pipelines and Their Importance in Automating Workflows•8 minutes
- Designing and Building Reusable Pipelines in Azure ML Studio•4 minutes
- Structuring Pipelines for Scalability and Efficiency•2 minutes
- Organizing Pipeline Steps (Data Ingestion, Feature Engineering, Model Training)•3 minutes
- Using Azure ML Studio for Both Training and Deployment Pipeline Creation•3 minutes
- How to Incorporate Custom Code into Azure ML Pipelines•5 minutes
- Best Practices for Versioning and Managing Dependencies in Pipelines•5 minutes
- Configuring Environment Variables for Pipeline Steps•5 minutes
- Connecting External Resources (Databases, Cloud Storage) in the Pipeline•5 minutes
- Scheduling Pipeline Runs with Triggers (Time-Based, Event-Driven)•4 minutes
- DEMO - Building a Custom Pipeline with Python Scripts in Azure ML Studio•17 minutes
- ADVANCED - Integrating Multiple Pipeline Components for Complex Workflows•5 minutes
- ADVANCED - Handling Failures and Retries in Pipelines•5 minutes
- ADVANCED - Using PipelineParameters for Dynamic Inputs•6 minutes
- DEMO - Using PythonScriptStep to Run Custom Python Scripts within Pipelines•8 minutes
1 reading•Total 10 minutes
- Conclusion to the Course 'Building, Evaluating, and Operationalizing ML Models'•10 minutes
3 assignments•Total 90 minutes
- Full Course Practice Assessment•15 minutes
- Machine Learning Pipelines (ML-OPS) - Assessment•15 minutes
- Full Course Assessment•60 minutes
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Frequently asked questions
Machine learning (ML) involves developing algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed. It is highly relevant because it can automate tasks, uncover patterns in large datasets, and improve decision-making in a variety of industries, from healthcare to finance, by providing predictive insights and optimizing operations.
The "Building, Evaluating, and Operationalizing ML Models" course focuses on the key stages of machine learning model development, from building and training models to evaluating their performance and optimizing them for real-world applications. It covers advanced topics such as model evaluation metrics, cross-validation, hyperparameter tuning, and pipeline creation. The course also explores advanced deployment strategies, such as real-time and batch inference, distributed training, and integrating models into production environments using Azure.
Upon completing this course, you will be able to build machine learning models using a variety of algorithms, evaluate their performance with metrics like cross-validation, and fine-tune models for optimal performance. You will also be able to design and manage end-to-end machine learning pipelines, deploy models using Azure, and track their performance in real-time, ensuring they can be continuously improved and maintained.
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