Cloud machine learning services are a key part of the modern computing landscape, offering organizations the opportunity to utilize machine learning solutions without providing advanced hardware like inference chips and optimized GPUs. Besides, they make machine learning frameworks easy to implement, deploy and configure. They offer various tools for face recognition, data visualization, application programming interfaces (APIs), predictive analytics, natural language processing (NLP), and deep learning. Azure Machine Learning is Microsoft's cloud-based service for machine learning implementations, which runs on top of the Microsoft Azure cloud and allows for building, deploying, and tracking machine learning and deep learning models with lots of capabilities and customizations.
TechClasss Azure Machine Learning online course introduces the primary machine learning tools available on the Azure ML studio. It focuses on standardized data analytics and machine learning implementation approaches, such as predictive modeling. By the end of this course, you will learn to build, train, deploy, automate, manage, and track enterprise-grade machine learning models from scratch in a simplified way using powerful Azure workspaces.
- Get familiar with the considerations regarding machine learning implementation and the concept of Machine Learning as a Service (MLaaS)
- Get familiar with Microsoft Azure's different AI and Machine Learning services
- Learn how to create an Azure account and how to set up an Azure ML workspace
- Get familiar with Azure ML Studio and different assets in the Studio
- Get familiar with different types of compute resources in Azure and how to use them for training and inference
- Get familiar with the Azure ML Designer service and how it works
- Gain hands-on experience with creating pipelines in Azure ML Designer and training and deploying models using this service
- Get familiar with the concept of AutoML and how Azure Automated ML provides this capability
- Gain hands-on experience with using Azure Automated ML to implement and deploy different machine learning models
- Learn how to set up, manage, and run Jupyter notebooks in Azure ML Studio
- Get familiar with the Azure ML Python SDK and how to use it to facilitate training and deploying machine learning models on top of Azure
- Learn how to prepare codes for deploying different machine learning models on production
Table of contentsChapter 1: Intro to Course
- 1.1. Welcome!
- 1.2. About TechClass Data Science Department
- 1.3. Learning Outcomes
- 1.4. Your Expectations, Goals, and Knowledge
- 1.5. Abbreviations
- 1.6. Copyright Notice
Chapter 2: Introduction
- 2.1. Basics of Machine Learning
- 2.2. Importance of Data
- 2.3. Types of Machine Learning
- 2.4. Quiz
- 2.5. Machine Learning Pipeline: Part I
- 2.6. Machine Learning Pipeline: Part II
- 2.7. Machine Learning Implementation
- 2.8. Why Cloud-based Services?
- 2.9. Microsoft Azure Cloud Services
- 2.10. Quiz
Chapter 3: Introduction to Azure Machine Learning
- 3.1. What is Azure ML?
- 3.2. Create an Azure ML Account
- 3.3. Getting Started with Azure ML
- 3.4. What is Azure ML Studio?
- 3.5. Azure ML Architecture and Concepts
- 3.6. Compute Targets
- 3.7. Quiz
Chapter 4: Azure ML Designer: Working with Data
- 4.1. Introduction to Azure ML Designer
- 4.2. Create a New Pipeline
- 4.3. Import Data: Manual Data Entry and Sample Datasets
- 4.4. Import Data: Create a Dataset
- 4.5. Visualize the Data
- 4.6. Prepare Data: Select Columns
- 4.7. Prepare Data: Clean Missing Data
- 4.8. Prepare Data: Apply Math Operations
- 4.9. Prepare Data: Split Data
Chapter 5: Azure ML Designer: Training and Deployment
- 5.1. Add Training Modules
- 5.2. Add Evaluation Modules
- 5.3. Set the Default Compute Target
- 5.4. Submit the Pipeline
- 5.5. View Evaluation Results
- 5.6. Create a Real-time Inference Pipeline
- 5.7. Create an Inferencing Cluster
- 5.8. Deploy the Real-time Endpoint
- 5.9. Test the Real-time Endpoint
- 5.10. Clean-up Resources
- 5.11. Algorithm Cheat Sheet for Azure ML Designer
- 5.12. Project 1: ML Designer
Chapter 6: Automated ML
- 6.1. Introduction to Automated ML
- 6.2. Getting Started with Automated ML
- 6.3. Create and Load a Dataset
- 6.4. Configure Experiment Run
- 6.5. Configure Task Type: Classification and Regression
- 6.6. Configure Task Type: Time-series Forecasting
- 6.7. Explore Models
- 6.8. Deploy the Best Model
- 6.9. Prevent Overfitting with Automated ML
Chapter 7: Automated ML: Tutorial on Forecasting Demand
- 7.1. Step 1: Set up Environment
- 7.2. Step 2: Load Dataset
- 7.3. Step 3: Run Experiment
- 7.4. Step 4: Deployment
- 7.5. Step 5: Use REST Endpoint
- 7.6. Project 2: Automated ML
Chapter 8: Azure ML Python SDK
- 8.1. Getting Started with Azure ML in Jupyter Notebooks
- 8.2. Azure ML Python SDK
- 8.3. Create and Connect to Workspace
- 8.4. Create an Experiment
- 8.5. Running a Script as an Experiment
- 8.6. Train a Model
- 8.7. Register Models
- 8.8. Prepare Your Code for Production
- 8.9. Deploy the Model
- 8.10. Project 3: Azure ML Python SDK
Chapter 9: What’s More?
- 9.1. Git Integration for Azure ML
- 9.2. Plan and Manage Costs for Azure ML
- 9.3. Next Steps
- 9.4. Quiz
Chapter 10: Final Tasks
- 10.1. Self-study Essay
Chapter 11: Finishing the Course
- 11.1. What We Have Learned
- 11.2. Where to Go Next?
- 11.3. Congrats! You did it!
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That was great to learn how to deploy the trained model. Very well written course.
informative course with lots of nice graphical elements
I liked this course a lot. Understood it properly. Thanks to TechClass AI team, who wrote this course.
The syllabus of the course takes you from basic level to advance level and you won't feel any trouble.
excellent course, azure is bit quirky, and some illogicalities with it's interface and naming components, but the material was great
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