AZ-900 Episode 16 | Azure Artificial Intelligence (AI) Services | Machine Learning Studio & Service

Adam Marczak - Azure for Everyone
21 Sept 202008:09

TLDRIn this episode, we delve into Azure's Artificial Intelligence services, focusing on Machine Learning Studio and the Machine Learning service. AI is defined as a branch of computer science that simulates human intelligence, while Machine Learning is a subset that allows software to learn from data to make predictions. Azure Machine Learning assists in the entire process of building a machine learning model, from training and validating to deploying as web services and monitoring. The service offers tools like Python or R notebooks, a visual designer for drag-and-drop model building, and AutoML for algorithm selection. Azure Machine Learning also manages compute resources and provides features for asset management, including datasets, experiments, and endpoints. The episode demonstrates the creation of a machine learning model using the Azure Machine Learning Studio's visual interface, highlighting the ease of building, training, and evaluating models without writing code. The workspace concept is central to managing all components of the machine learning service. Finally, the episode mentions the transition from the older Machine Learning Studio to the new, more feature-rich Azure Machine Learning service.

Takeaways

  • 📚 AI is a branch of computer science that simulates human intelligence using 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 models and is supported by a set of tools including notebooks and a visual designer.
  • 📈 The process of building a machine learning model includes training, packaging, validating, and deploying the model as web services.
  • 🔍 AutoML is a feature that allows for testing various algorithms on data to find the best performer.
  • 📊 Pipelines in Azure Machine Learning enable an end-to-end solution for building machine learning models.
  • 🌐 Azure Machine Learning Studio is a web-based interface for managing the entire Azure Machine Learning service.
  • 💾 Assets in Azure Machine Learning include datasets, experiments, pipelines, and deployed model endpoints.
  • 🧠 Compute resources are managed by Azure, allowing users to focus on model development without worrying about infrastructure.
  • 🔧 The Designer feature provides a visual, drag-and-drop interface for building machine learning models without coding.
  • 🔗 Azure Machine Learning integrates with other services like Azure Blob Storage and Azure File Share for data management.

Q & A

  • What is the main focus of the AZ-900 Episode 16?

    -The main focus of the AZ-900 Episode 16 is to discuss Azure Artificial Intelligence (AI) Services, specifically Machine Learning Studio and Service.

  • How is AI defined in the context of this episode?

    -In this episode, AI is defined as a branch of computer science where software is used to simulate human intelligence and capabilities.

  • What is the role of Machine Learning in AI?

    -Machine Learning is a subcategory of AI where the software is taught to draw conclusions and make predictions based on data, a process known as building a model.

  • What is the key service in Azure for building machine learning models?

    -The key service in Azure for building machine learning models 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 the 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 model building process?

    -Azure Machine Learning provides tools such as notebooks written in Python or R, a visual designer for drag-and-drop model building in the browser, and automated machine learning (AutoML) for algorithm selection and parameter tuning.

  • What is Azure Machine Learning Studio?

    -Azure Machine Learning Studio is a web-based visual interface for managing the entire Azure Machine Learning service, including notebooks, Automated ML, and a visual designer.

  • How does the Azure Machine Learning service manage compute resources?

    -Azure Machine Learning manages compute resources by allowing users to train, package, validate, and deploy models without worrying about the underlying Azure infrastructure and resources.

  • What is the purpose of pipelines in Azure Machine Learning?

    -Pipelines in Azure Machine Learning allow users to build an end-to-end process for machine learning model development, whether using notebooks, the designer, or automated tools.

  • How does the Azure Machine Learning workspace help in managing machine learning services?

    -The Azure Machine Learning workspace is a top-level resource that ties together all components of the machine learning service, including compute resources, permissions, runs, pipelines, experiments, history, and connections to external services.

  • What is the difference between the old Machine Learning Studio and the new Azure Machine Learning service?

    -The old Machine Learning Studio is no longer actively developed, whereas the new Azure Machine Learning service offers a similar studio experience with additional features and is the recommended service for new customers.

  • What additional features does the new Azure Machine Learning service offer?

    -The new Azure Machine Learning service offers features such as end-to-end cloud-based platform for creating, managing, and publishing machine learning models, management of data and compute resources, and integration with other machine learning pipelines for orchestrating model training and deployment.

Outlines

00:00

📚 Introduction to Azure AI and Machine Learning Services

This paragraph introduces the audience to the world of artificial intelligence (AI) and machine learning (ML) within the Azure platform. It explains AI as a branch of computer science that simulates human intelligence, while ML is a subset of AI that involves teaching software to make predictions based on data. The key service in Azure for ML is Azure Machine Learning, which assists in building, training, packaging, validating, and deploying ML models as web services. The paragraph also touches on the tools provided by Azure Machine Learning, such as notebooks, a visual designer for drag-and-drop model building, and automated machine learning (AutoML) for algorithm selection. Additionally, it mentions the management of compute resources and the concept of ML pipelines for an end-to-end solution in building ML models. The speaker then demonstrates navigating the Azure portal to access the Machine Learning workspace and studio, highlighting the ease of building ML models through the designer interface.

05:02

🚀 Executing and Managing Azure ML Pipelines

The second paragraph delves into the process of executing and managing machine learning pipelines in Azure. It guides through submitting a pipeline for execution by creating a new experiment or choosing an existing one. The duration of the model training process can vary based on the complexity and the compute resources selected. The Azure interface provides real-time feedback on the model building process. For data scientists, the platform offers in-depth evaluation results, logs, outputs, and a scoring model section for detailed data set analysis. The paragraph summarizes the Azure Machine Learning service as a comprehensive, cloud-based platform for creating, managing, and publishing ML models. It emphasizes the Machine Learning workspace as a central resource that integrates compute resources, permissions, runs, pipelines, experiments, history, and connections to external services. The paragraph also clarifies the difference between the old 'Machine Learning Studio' and the current 'Azure Machine Learning', noting that the former is no longer actively developed. The key features of Azure Machine Learning service, including notebooks, automated ML, visual designer, data and compute resource management, and integration into ML pipelines, are outlined. The speaker concludes by directing viewers to their website for more materials and hints at the topic of the next episode, which will focus on serverless computing in Azure.

Mindmap

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is the overarching theme, as it discusses the various services and tools provided by Azure for developing AI applications.

💡Machine Learning

Machine Learning is a subset of AI that involves the use of data and algorithms to enable machines to learn and improve from experience without being explicitly programmed. The video emphasizes machine learning as a core component of AI, where software is taught to make predictions and draw conclusions based on data.

💡Azure Machine Learning

Azure Machine Learning is a cloud-based service provided by Microsoft that enables the building, training, and deployment of machine learning models. The video discusses how this service assists in the entire process of creating machine learning models, from training to deployment and monitoring.

💡Model

In the context of machine learning, a model refers to a mathematical representation of a system or process that is trained using data. The video explains that building a model involves training it with data, validating, and then deploying it as a web service for predictions.

💡Web Services

Web services in the video refer to the deployment of machine learning models as accessible endpoints over the web. These services allow for the integration of the models into applications for making predictions or decisions based on input data.

💡Automated ML (AutoML)

Automated ML, or AutoML, is a feature within Azure Machine Learning that automates the process of selecting the best machine learning model by applying various algorithms to the data and determining which one performs the best. The video highlights AutoML as a time-saving feature for data scientists.

💡Pipelines

Pipelines in the context of Azure Machine Learning are end-to-end workflows that automate the machine learning lifecycle, from data preparation to model training and deployment. The video demonstrates how pipelines help in building a complete machine learning solution.

💡Machine Learning Studio

Machine Learning Studio is a web-based visual interface within Azure Machine Learning that allows for the management and execution of machine learning workflows. The video shows how users can navigate through the studio to access various tools and features for machine learning.

💡Compute Resources

Compute resources in the video refer to the virtual machines and processing power used to train, package, validate, and deploy machine learning models. Azure Machine Learning manages these resources, simplifying the process for users.

💡Notebooks

Notebooks in Azure Machine Learning are interactive computing environments that support languages like Python and R. They are used to write and test code, create scripts, and build machine learning models as demonstrated in the video.

💡Asset Management

Asset management within Azure Machine Learning involves organizing and managing the different components of a machine learning project, such as datasets, experiments, models, and endpoints. The video mentions that Azure Machine Learning provides tools for managing these assets effectively.

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 using software.

Machine Learning is a subcategory of AI that involves software drawing conclusions and making predictions based on data.

Azure Machine Learning is a key service for building machine learning models, consisting of training, packaging, validating, and deploying models as web services.

Azure Machine Learning provides tools such as notebooks in Python or R and a visual designer for building models through a drag-and-drop interface.

The service manages compute resources for training, packaging, validating, and deploying models, abstracting away the underlying Azure infrastructure.

AutoML is a feature of Azure Machine Learning that automates the process of selecting the best algorithm for a given dataset.

Pipelines in Azure Machine Learning allow for an end-to-end solution for building and managing machine learning models.

Azure Machine Learning Studio is a web-based visual interface for managing the entire Azure Machine Learning service.

Notebooks in Azure Machine Learning Studio enable users to create scripts or try out Microsoft-provided samples to build machine learning models.

AutomatedML simplifies the process of selecting the best model by allowing users to apply various algorithms and tweak parameters.

The Designer feature in Azure Machine Learning Studio allows for visual model building with drag-and-drop functionality, eliminating the need for coding.

Asset management in Azure Machine Learning includes datasets, experiments, pipelines, models, and endpoints for deployed models.

Compute targets in Azure Machine Learning enable users to create virtual machines for running their machine learning workflows.

Users can train models, clean data, and split datasets for training and evaluation within the visual interface.

Azure Machine Learning provides real-time feedback on the model building process through its user interface.

Data scientists can evaluate model results, check logs, and visualize datasets directly within the Azure Machine Learning Studio.

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 in Azure Machine Learning that ties together all components of the service.

Azure Machine Learning Studio is the web portal used for end-to-end management of the workspace and includes additional features compared to the older Machine Learning Studio.

The episode concludes with a teaser for the next episode, which will cover serverless computing in Azure.

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