Azure Machine Learning & MLOps : Beginner to Advance

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Mohammad Ghodratigohar (MG)

17:40:17

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1. Azure MLOps

1.1 The GitHub repo with codes I used for this MLOps series.html

1. Complete Intro to Azure Machine Learning Service.mp4

26:12

2.1 The GitHub repo with codes I used for this MLOps series.html

2. Intro to Azure DevOps.mp4

08:25

3.1 The GitHub repo with codes I used for this MLOps series.html

3. Setting up Azure DevOps Configurations.mp4

18:54

4.1 The GitHub repo with codes I used for this MLOps series.html

4. Create & Deploy Infrastructure as Code Pipeline.mp4

33:50

5.1 The GitHub repo with codes I used for this MLOps series.html

5. CI Pipeline ( Continuous Integration) for ML.mp4

22:20

6.1 The GitHub repo with codes I used for this MLOps series.html

6. CI Pipeline ( Continuous Integration) for ML.mp4

32:47

7.1 The GitHub repo with codes I used for this MLOps series.html

7. Automated Training with CI Pipeline.mp4

20:52

8.1 The GitHub repo with codes I used for this MLOps series.html

8. CD Pipeline (Continuous Deployment) for Staging.mp4

34:12

9.1 The GitHub repo with codes I used for this MLOps series.html

9. CD Pipeline(Continuous Deployment)for Production.mp4

28:35

10.1 The GitHub repo with codes I used for this MLOps series.html

10. Testing End to End MLOps Pipelines.mp4

10:17

11.1 Source Article and Repo.html

11.2 The Github repo utilized in the video.html

11. Extra 1 Azure MLOps with GitHub Actions & Azure Machine Learning.mp4

38:56

12.1 Source Github repo for Databricks DevOps.html

12.2 The Github repo utilized in the video.html

12. Extra 2 Databricks MLOps With GitHub Actions & MLflow.mp4

01:18:30

2. Azure Machine Learning Highly Demanded Capabilities

1.1 The GitHub repo for open-source Responsible AI toolbox.html

1. Responsible AI in Action - Part 1.mp4

17:21

2.1 Generate a Responsible AI dashboard (preview) in the studio UI.html

2.2 Responsible AI Dashboard and Scorecard.html

2. Responsible AI in Action - Part 2.mp4

20:43

3. Azure Machine Learning Pipeline.mp4

33:47

4.1 Showcased article about bringing feature store to Azure.html

4.2 The GitHub utilized in the video.html

4. Feature Store (Feast) in Azure (Machine Learning).mp4

46:39

5.1 Showcased article about bringing Ray and Dask to Azure machine learning.html

5.2 The GitHub utilized in the video.html

5. Ray and Dask in Azure Machine Learning.mp4

23:20

6.1 AutoML for computer vision in Azure Machine Learning documentation.html

6. Auto ML for Computer Vision in Azure Machine Learning.mp4

26:51

7.1 The GitHub repo of utilized code in the video.html

7. Differential Privacy For Machine Learning In Action (Sensitive Data).mp4

23:55

8.1 Databricks Step in Azure ML Documentation.html

8.2 The GitHub repo for Databricks Step in Azure ML.html

8. Integrate Azure Databricks with Azure Machine Learning.mp4

30:09

9.1 AutoML for NLP in Azure Machine Learning documentation.html

9. AutoML for Natural Language Processing (NLP) in Azure Machine Learning.mp4

18:05

10.1 The example code utilized in the video.html

10. Train and Score Thousands of Machine Learning Models in Parallel ( Many Models i.mp4

31:25

11.1 The example code and docs utilized in the video.html

11. Why do Machine learning models fail (Data Drift Monitoring in Azure).mp4

24:16

12.1 Codes and Documentation.html

12. Integrating Azure Synapse with Azure Machine Learning.mp4

21:57

13.1 Reference code.html

13. Deploy Multi Model Endpoint in Azure Machine Learning.mp4

14:28

14.1 Reference code.html

14. Rollout (Blue-Green) Real-Time Machine Learning Model Deployment in Azure.mp4

16:58

15. Consume Azure Machine Learning models in Power BI.mp4

12:48

16.1 Github Repo utilized in this video.html

16. Use PowerApps to Create Apps for Deployed Azure Machine Learning Models.mp4

19:18

17.1 Reference docs.html

17. Attach Your OnPrem Kubernetes to Azure Machine Learning Using Azure Arc.mp4

29:25

18. Components in Azure Machine Learning Pipelines.mp4

19:25

19. Being Platform Agnostic with MLflow in Azure Machine Learning.mp4

15:56

20.1 Sample notebook used in the video.html

20. Deploy Spark ML model as Web Service Using Azure Machine Learning.mp4

09:55

21.1 Reference notebook utilized in the video.html

21. Azure Machine Learning Environments.mp4

16:08

22.1 Reference doc.html

22.2 Utilized repo.html

22. Scalable & Managed Batch Prediction with Azure Machine Learning.mp4

32:49

23. Execute Azure Machine Learning pipelines in Azure Data Factory or Synapse.mp4

20:11

24.1 The notebook example utilized in the video.html

24. Extra Train Machine learning model once and deploy it anywhere with ONNX.mp4

20:47

25.1 Point to Site VPN.html

25.2 The example code utilized in the video.html

25. Extra Azure Machine Learning Networking (Hands-on).mp4

01:05:54

26. Extra AutoML Comparison in Databricks VS Azure Machine Learning.mp4

40:28

27.1 Reference repository used in the video.html

27. Extra Machine Learning Lineage with Microsoft Purview.mp4

53:29

Description

The most compressive course covering MLOps and machine learning on Azure | Zero to Hero

What You'll Learn?

How to use Azure Machine Learning from Development to Production
How to Use Azure DevOps for Continuous Integration, continuous deployment on Machine Learning
How to automate End 2 End machine Learning Solution on Azure
How to Deploy Machine learning Models on Azure (Azure Container Instances, Azure Kubernetese Services, managed endpoints)
Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning
Bests practices and highly demanded capabilities of machine learning on Azure Cloud

Who is this for?

Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud

Data scientists who want to earn DP-100 Certification

Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path

Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud

What You Need to Know?

Nice to have familiarity with basics of Machine Learning

If you want to practice the contents, free or paid subscription to Microsoft Azure is required

More details

Description
AÂ course instructed by me and my digital twin if:
You are looking for a comprehensive, engaging, and fun course for mastering Azure Machine learning ( up to even advanced industry-required topics) plus fully hands-on end-to-end implantation of MLOps ( DevOps for Machine learning on Azure). If yes, then this is the right and very unique course for you!Â
Machine Learning Operations (MLOps) is a rapidly growing culture nad highly demanded in the industry with a set of principles, and guidelines defined in the machine learning world to deploy a machine learning model into production.
Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.
You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models.

Key points about this course

Very detailed in-depth and comprehensive coverage
This course will help you prepare for entry into this hot career path of Machine Learning and MLOps
The course is regularly updated with recent features
Best practices and impactful features of Azure ML (e.g, Explainable AI)Â with its tricks are all covered
Contains some extra videos relevant to Azure Machine Learning and Databricks (Apache Spark)

Who this course is for:
Anyone who wants to learn more about Data Science and Machine Learning specifically on Cloud
Data scientists who want to earn DP-100 Certification
Developers who want to enter AI Cloud Solution Architect or Machine Learning Engineer career path
Anyone who wants to start a career in or wants to learn about the Machine Learning and MLOPs on Cloud

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Mohammad Ghodratigohar (MG) is a Microsoft data scientist and AI cloud solution architect with years of hands-on knowledge and expertise in leveraging advanced AI techniques to solve business problems. He designed, developed, and led various machine learning projects mainly in healthcare through private sector companies and hospitals in computer vision, signal processing, and geospatial analysis fields. Mohammad has a MSc degree in biomedical engineering with a specialization and journal publications in machine learning and healthcare.

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