AZ-900 Episode 16 | Azure Artificial Intelligence (AI) Services | Machine Learning Studio & Service
TLDRIn this episode, we delve into the realm of Azure Artificial Intelligence (AI) Services, focusing on Machine Learning Studio and Service. We begin with an overview of AI and Machine Learning (ML), explaining how ML enables software to learn from data and make predictions. Azure Machine Learning is highlighted as the key service for building ML models, offering tools such as notebooks in Python or R, a visual designer for drag-and-drop model building, and Automated ML for algorithm selection and parameter tuning. The episode demonstrates the end-to-end process of model training, validation, deployment, and monitoring within Azure Machine Learning Studio, emphasizing the ease of use and the comprehensive management of compute resources, data stores, and model deployment. The Azure Machine Learning workspace is introduced as the central hub for all ML operations, including asset management and external service connections. The episode concludes with a quick demonstration of building a machine learning model using the visual interface, showcasing the real-time model building process and the evaluation of results. The Azure Machine Learning service is presented as a full-fledged, cloud-based platform for creating, managing, and publishing ML models, with a strong emphasis on its user-friendly interface and robust capabilities.
Takeaways
- 📚 AI is a branch of computer science that simulates human intelligence, while machine learning is a subset of AI that involves teaching software to make predictions based on data.
- 🛠️ Azure Machine Learning is a key service for building machine learning models, which includes training, packaging, validating, and deploying models as web services.
- 📊 The process of building a machine learning model involves training, validating, and deploying the model, followed by monitoring and retraining for improved results.
- 💻 Azure Machine Learning provides tools such as notebooks in Python or R, a visual designer for drag-and-drop model building, and automated machine learning (AutoML) for algorithm selection.
- 🔍 AutoML allows users to test various algorithms on their data to find the best performer, which can then be deployed as a web service.
- 📈 Azure Machine Learning Studio is a web-based interface for managing the entire Azure Machine Learning service, including notebooks, Automated ML, and the visual designer.
- 🧠 The Azure Machine Learning workspace is a top-level resource that brings together compute resources, permissions, experiments, pipelines, and model deployments.
- 🔧 Users can manage compute resources and data stores such as Azure Blob Storage and Azure File Share through Azure Machine Learning.
- 📝 The Designer feature in Azure Machine Learning allows for visual building of machine learning models with a drag-and-drop interface, without writing code.
- 🔗 Azure Machine Learning integrates with other services for asset management, including datasets, experiments, pipelines, and endpoints for model deployment.
- ⚙️ A compute target, such as a virtual machine, is created to run the machine learning workflow, simplifying the process of model training and evaluation.
Q & A
What is the main focus of the discussed episode?
-The main focus of the discussed episode is on Azure Artificial Intelligence (AI) Services, specifically Machine Learning Studio and Machine Learning Service.
What is the general definition of AI?
-AI is a branch of computer science where software is used to simulate human intelligence and capabilities.
How is Machine Learning related to AI?
-Machine Learning is a subcategory of AI where software is taught to draw conclusions and make predictions based on data.
What is the key service in Azure for building a Machine Learning model?
-The key service in Azure for building a Machine Learning model is called Azure Machine Learning.
What are the typical steps involved in building a Machine Learning model?
-The typical steps include training the model based on data, packaging and validating the model, and if results are satisfactory, deploying the model as a web service, monitoring, and retraining the model for better results.
What tools does Azure Machine Learning provide to assist in the process of building Machine Learning models?
-Azure Machine Learning provides tools like notebooks written in Python or R, a visual designer for drag-and-drop model building, and automated machine learning (AutoML) for algorithm selection and parameter tuning.
What is the role of a Machine Learning workspace in Azure?
-A Machine Learning workspace in Azure is a top-level resource that ties everything together, including compute resources, permissions, runs, pipelines, experiments, history, and connections to external services.
How does Automated ML (AutoML) assist in the model building process?
-AutoML assists by allowing users to apply various algorithms to their data, tweak parameters, and identify the best performing model to deploy as a web service.
What is the purpose of the visual designer in Azure Machine Learning?
-The visual designer allows users to build machine learning models using a drag-and-drop interface without writing any code.
What is a compute target in the context of Azure Machine Learning?
-A compute target is a virtual machine or compute resource that is used to run machine learning workflows.
How can users manage their machine learning models and assets in Azure?
-Users can manage their machine learning models and assets through the Azure Machine Learning workspace, which provides features for asset management, compute resource management, and data store connections.
What is the difference between the old Machine Learning Studio and the one discussed in the episode?
-The old Machine Learning Studio is no longer actively developed, whereas the one discussed is a part of the new Azure Machine Learning service with additional features and an integrated studio experience.
Outlines
📚 Introduction to Azure AI and Machine Learning
The first paragraph introduces the viewer to the topic of artificial intelligence products within Azure. It explains that AI is a branch of computer science focused on simulating human intelligence, and machine learning is a subcategory of AI that involves teaching software to make predictions based on data. The key service in Azure for machine learning is Azure Machine Learning, which assists in building models, training, packaging, validating, and deploying these models as web services. The paragraph also touches on the tools provided by Azure Machine Learning, such as notebooks in Python or R, a visual designer for a drag-and-drop experience, and the concept of automl for automated algorithm selection. Additionally, it mentions the feature of pipelines for end-to-end model building and the Azure Machine Learning Studio for a web-based interface to manage the entire service.
🚀 Azure Machine Learning Service Overview and Workflow
The second paragraph delves into the practical aspects of using Azure Machine Learning Service. It describes the process of submitting a machine learning pipeline for execution, which involves creating a new experiment and running the pipeline, with the duration depending on the complexity and chosen machine. The paragraph emphasizes the real-time visual feedback provided by the UI during model building. For data scientists, the service offers the ability to check evaluation results, logs, outputs, and visualize datasets used for training. It summarizes the Azure Machine Learning Service as an end-to-end cloud-based platform for creating, managing, and publishing machine learning models. The Machine Learning workspace is highlighted as a top-level resource that ties together all components, including compute resources, permissions, runs, pipelines, experiments, and model deployments. The paragraph also clarifies the difference between the old 'Machine Learning Studio' and the current 'Azure Machine Learning Studio', noting that the former is no longer actively developed. Key features of the service include notebooks, automated ML, visual designer for code-free pipeline building, data and compute resource management, and integration into machine learning pipelines for orchestration of tasks.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Machine Learning
💡Azure Machine Learning
💡Model Training
💡Web Services
💡Automated ML (AutoML)
💡Pipelines
💡Machine Learning Studio
💡Compute Resources
💡Asset Management
💡Linear Regression
Highlights
AZ-900 Episode 16 focuses on Azure Artificial Intelligence (AI) Services, specifically Machine Learning Studio & Service.
AI is defined as a branch of computer science that simulates human intelligence and capabilities with software.
Machine Learning is a subcategory of AI where software is taught to make predictions based on data.
Azure Machine Learning is a key service for building machine learning models.
The process of building a machine learning model includes training, packaging, validating, and deploying as web services.
Azure Machine Learning provides tools such as notebooks in Python or R and a visual designer for a drag-and-drop experience.
Automated Machine Learning (AutoML) allows for the testing of various algorithms on data to find the best performer.
Pipelines feature in Azure Machine Learning enables end-to-end building of machine learning models.
Azure Machine Learning Studio is a web-based visual interface for managing the entire Azure Machine Learning service.
Notebooks allow users to create scripts or try out Microsoft-provided samples for building machine learning models.
AutomatedML simplifies the process of selecting the best algorithm for a given dataset and deploying it as a web service.
The Designer in Azure Machine Learning offers a visual, drag-and-drop method for building machine learning models without coding.
Asset management in Azure Machine Learning includes datasets, experiments, pipelines, models, and endpoints.
Compute resources and data stores like Azure Blob Storage and Azure File Share are managed within Azure Machine Learning.
A machine learning model can be trained using a simple workflow with drag-and-drop data selection and cleaning steps.
Linear regression is used in the example to forecast prices, showcasing the ease of model training in Azure.
Experiments can be created and submitted to run, with the UI showing real-time model building progress.
Data scientists can evaluate results, check logs, and visualize datasets for further analysis after model training.
Azure Machine Learning Service is an end-to-end cloud-based platform for creating, managing, and publishing machine learning models.
Machine Learning Workspace is a top-level resource that ties together all components of the Azure Machine Learning service.
The old Machine Learning Studio is different and no longer actively developed; new customers are encouraged to use the new Azure Machine Learning.
Key features of Azure Machine Learning Service include notebooks, Automated ML, a visual designer, and integrated pipelines for model management.