Designing and Implementing a Data Science Solution on Azure (DP-1...
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- Language English
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Designing and Implementing a Data Science Solution on Azure (DP-100): Train and Deploy Models
https://WebToolTip.com
Released 11/2025
By Deepak Goyal
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 31m | Size: 312 MB
Master Azure ML training, pipelines, and deployment aligned with the DP-100 exam. This course will teach you how to run jobs, manage models, and deploy solutions for production-ready machine learning.
Developing machine learning solutions in Azure can be a daunting task; from training models and managing pipelines to deploying endpoints at scale, numerous components must collaborate seamlessly. In this course, Designing and Implementing a Data Science Solution on Azure (DP-100): Train and Deploy Models, you’ll gain the ability to run, manage, and deploy models effectively using Azure Machine Learning, while preparing for key objectives of the DP-100 certification exam. First, you’ll explore how to run model training scripts, configure compute and environments, and track experiments with MLflow. Next, you’ll discover how to implement training pipelines by creating components, chaining steps, and automating recurring workflows. Finally, you’ll learn how to manage models with MLflow, deploy them to both online and batch endpoints, test deployed services, and troubleshoot deployments at scale. When you’re finished with this course, you’ll have the skills and knowledge of Azure Machine Learning model training, pipeline orchestration, and deployment needed to deliver production-ready machine learning solutions on Azure, and you’ll be well-prepared for the DP-100 certification exam.
Files:
[ WebToolTip.com ] Designing and Implementing a Data Science Solution on Azure (DP-100) - Train and Deploy Models- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Run Model Training Scripts
- 1. Understanding Jobs and Data in Azure ML.mp4 (7.7 MB)
- 1. Understanding Jobs and Data in Azure ML.vtt (4.8 KB)
- 2. Demo - Consuming Data in Azure ML Jobs.mp4 (19.6 MB)
- 2. Demo - Consuming Data in Azure ML Jobs.vtt (8.2 KB)
- 3. Demo - Configuring Compute for Model Training.mp4 (10.2 MB)
- 3. Demo - Configuring Compute for Model Training.vtt (4.7 KB)
- 4. Demo - Setting up Environments for Training Runs.mp4 (15.8 MB)
- 4. Demo - Setting up Environments for Training Runs.vtt (7.3 KB)
- 5. MLflow for Experiment Tracking.mp4 (2.9 MB)
- 5. MLflow for Experiment Tracking.vtt (2.6 KB)
- 6. Demo - Running and Tracking Training Jobs with MLflow.mp4 (12.4 MB)
- 6. Demo - Running and Tracking Training Jobs with MLflow.vtt (4.3 KB)
- 7. Demo - Troubleshooting Job Runs with Logs.mp4 (9.4 MB)
- 7. Demo - Troubleshooting Job Runs with Logs.vtt (3.5 KB)
- 1. What Are Pipelines and Components.mp4 (3.9 MB)
- 1. What Are Pipelines and Components.vtt (3.5 KB)
- 2. What Is a Custom Component.mp4 (2.9 MB)
- 2. What Is a Custom Component.vtt (2.4 KB)
- 3. Demo - Creating Custom Components in Azure ML.mp4 (35.1 MB)
- 3. Demo - Creating Custom Components in Azure ML.vtt (10.8 KB)
- 4. Demo - Building Machine Learning Pipelines.mp4 (22.2 MB)
- 4. Demo - Building Machine Learning Pipelines.vtt (7.8 KB)
- 5. Demo - Passing Data between Pipeline Steps.mp4 (6.7 MB)
- 5. Demo - Passing Data between Pipeline Steps.vtt (3.2 KB)
- 6. Demo - Running and Scheduling Pipelines.mp4 (8.4 MB)
- 6. Demo - Running and Scheduling Pipelines.vtt (3.2 KB)
- 7. Demo - Monitoring and Troubleshooting Pipeline Runs.mp4 (9.2 MB)
- 7. Demo - Monitoring and Troubleshooting Pipeline Runs.vtt (3.7 KB)
- 1. Understanding MLmodel Files and Signatures.mp4 (6.3 MB)
- 1. Understanding MLmodel Files and Signatures.vtt (0.0 KB)
- 2. Demo - Implement MLmodel Files and Signatures.mp4 (32.9 MB)
- 2. Demo - Implement MLmodel Files and Signatures.vtt (8.9 KB)
- 3. Packaging Feature Retrieval Specifications.mp4 (4.3 MB)
- 3. Packaging Feature Retrieval Specifications.vtt (3.2 KB)
- 4. Demo - Registering and Versioning MLflow Models.mp4 (11.4 MB)
- 4. Demo - Registering and Versioning MLflow Models.vtt (5.8 KB)
- 5. Feature Retrieval and Responsible AI.mp4 (6.1 MB)
- 5. Feature Retrieval and Responsible AI.vtt (4.4 KB)
- 6. Demo - Assessing Models with Responsible AI Principles.mp4 (21.8 MB)
- 6. Demo - Assessing Models with Responsible AI Principles.vtt (10.5 KB)
- 1. Online vs. Batch Deployment in Azure ML.mp4 (6.2 MB)
- 1. Online vs. Batch Deployment in Azure ML.vtt (4.4 KB)
- 2. Demo - Configuring Settings for Online Deployment.mp4 (6.0 MB)
- 2. Demo - Configuring Settings for Online Deployment.vtt (4.2 KB)
- 3. Demo - Deploying Models to Online Endpoints.mp4 (409.8 KB)
- 3. Demo - Deploying Models to Online Endpoints.vtt (0.3 KB)
- 4. Demo - Testing Online Deployed Services.mp4 (1.5 MB)
- 4. Demo - Testing Online Deployed Services.vtt (1.2 KB)
- 5. Demo - Batch Deployment with Compute Configuration.mp4 (4.2 MB)
- 5. Demo - Batch Deployment with Compute Configuration.vtt (3.0 KB)
- 6. Demo - Running Batch Scoring Jobs on Batch Endpoints.mp4 (8.3 MB)
- 6. Demo - Running Batch Scoring Jobs on Batch Endpoints.vtt (4.4 KB)
- Bonus Resources.txt (0.1 KB)
- playlist.m3u (1.9 KB)
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