Create and Publish Pipelines for Batch Inferencing with Azure
Focused View
Kishan Iyer
2:44:46
17 View
01. Course Overview.mp4
02:08
02. Course Prerequisites and Outline.mp4
03:13
03. Introducing Azure Machine Learning.mp4
06:33
04. Datasets in Azure ML.mp4
02:55
05. Azure ML Terms and Concepts.mp4
06:41
06. Demo-Creating an Azure ML Workspace.mp4
07:07
07. Demo-Create a Compute Cluster.mp4
03:19
08. Demo-Exploring the Designer.mp4
04:14
09. Demo-Loading and Configuring a Dataset.mp4
09:04
10. Demo-Summarizing a Dataset.mp4
05:09
11. Demo-Running a Pipeline.mp4
03:10
12. Common Machine Learning Workflows.mp4
07:46
13. Demo-Marking Columns as Categorical.mp4
04:33
14. Demo-Handling Missing Data.mp4
07:30
15. Demo-Applying One-hot Encoding.mp4
03:37
16. Demo-Standardizing Numeric Fields.mp4
05:11
17. Demo-Creating Training and Test Sets.mp4
05:29
18. Demo-Training and Evaluating a Model.mp4
05:10
19. Demo-Examining the Evaluation Metrics.mp4
06:38
20. Hyperparameter Tuning.mp4
03:32
21. Demo-Implementing Hyperparameter Tuning.mp4
05:31
22. Demo-Evaluating Combinations of Hyperparameters.mp4
05:36
23. Batch Inference Pipelines.mp4
02:34
24. Demo-Re-building a Model Training Pipeline.mp4
03:39
25. Demo-Creating and Analyzing a Batch Inference Pipeline.mp4
06:39
26. Demo-Publishing and Running a Batch Inference Pipeline.mp4
08:05
27. Inference Pipelines in Azure ML.mp4
02:07
28. Demo-Creating a Pipeline for Inferences.mp4
07:32
29. Demo-Configuring and Testing an Inference Pipeline.mp4
08:03
30. Demo-Deploying an Inference Pipeline.mp4
08:32
31. Demo-Consuming an Inference Pipeline.mp4
02:28
32. Summary and Further Study.mp4
01:01
Description
This course will teach you how to use the Azure Machine Learning service to build and run ML pipelines using the drag-and-drop designer interface. You will cover the publishing and deployment of pipelines for batch and real-time inferences.
What You'll Learn?
- discover the Azure ML service and the breadth of features it has to offer when it comes to building and managing ML models
- explore a number of data transformations which can be applied to a dataset by simply dragging and dropping various modules into the pipeline
- see that the handling of missing values, the standardization of numeric features as well as one-hot encoding for categorical fields can all be accomplished without writing a line of code
- use the pipeline to make predictions on new data
A machine learning model goes through a number of stages in its lifecycle; from training, to evaluation, through deployment and then maintenance. While there are a number of tools available for these stages, their management can become overwhelming even for the seasoned ML engineer.
In this course, Create and Publish Pipelines for Batch Inferencing with Azure, you'll experience an intuitive and easy-to-maintain environment for all things ML and focus on building and running pipelines for batch inferences:
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Kishan Iyer
Instructor's CoursesI have a Masters in Computer Science from Columbia University and have worked previously as a developer and DevOps engineer. I now work at Loonycorn which is a studio for high-quality video content. My interests lie in the broad categories of Big Data, ML and Cloud.
Pluralsight
View courses PluralsightPluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.
- language english
- Training sessions 32
- duration 2:44:46
- level average
- Release Date 2023/12/09
