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ML for QA automation
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Module 8
Implementation: Building the ML Pipeline
1
Fetching and Versioning Raw Data
Create a script to fetch and version the primary raw data source for your chosen project.
Parsing Data for Information Extraction
Implement parsing logic to extract relevant information from the raw data.
3
Data Preprocessing for Machine Learning
Build a preprocessing pipeline that transforms parsed data into a model-ready feature set.
Model Training and Validation
Train and validate your model on the prepared dataset, logging key metrics.
5
Saving and Loading Machine Learning Models
Persist the trained model and its associated artifacts (e.g., vectorizer, scaler).
6
Building Your Prediction Engine
Implement a prediction function or class that encapsulates the full inference pipeline.
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Project Definition and Scoping
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Deployment: Shipping Your Tool