Lecture 03 - Feature Selection, Model Training, Batch Inference Pipelines, and the Model Registry
This is part of the Serverless ML free online course 2022.
This is the third lecture in the course:
Feature Selection, Model Training, Batch Inference Pipelines, and the Model Registry.
For more information visit;
https://www.serverless-ml.org
https://github.com/featurestoreorg/se...
Chapters:
00:00 Intro
00:20 What will we cover in this lecture
00:43 Feature Selection
07:30 Feature Selection with Scikit-learn
10:45 Feature Selection using Mutual Information: X-Ray at Uber
12:24 Feature Views & Model-Specific Transformations
17:05 Join Features together to create a Feature View
19:09 Create Training Data with a Feature View - PiT Correct JOINs
21:56 Create a Feature View
24:10 Create a Feature View from your Selected Features
25:35 Feature View Offline API: Create Training Data or Batch Inference Data
27:27 Training Data Splits - Train/Validation/Test Sets
29:03 Feature View Offline API: Create Training Data
32:44 Random or Time-Series Split into Train/Test Sets?
36:03 Feature View Online API: Retrieve Feature Vectors for Online Models
39:21 Feature Selection Pipeline
40:23 Model-Specific Transformations
43:27 Transformations as part of the Feature View
44:30 Transformations applied transparently when using the Feature View
46:37 Training Pipelines
47:00 If possible don't Train! Use Pretrained Models: Transfer Learning
50:18 Training Pipelines
51:10 Transformations in Scikit-Learn Transformation Pipeliens
55:14 Hyperparameters Tuning
59:34 Experiment Tracking
1:00:51 Model training/eval - We follow this pattern in this course
1:01:38 Publishing the Model to the Model Registry
1:05:00 Hopsworks - FEature And Model Artifact Store & Metadata
1:07:15 Labeling Service
1:10:30 Data Centric AI
1:14:28 Batch Inference Pipelines
1:15:02 Analytical ML Systems
1:18:45 Batch Inference Pipeline
1:20:20 Conclusion
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