MLOps Template, Part 3 - MLflow + MinIO

In the last part, we saw our pipeline at the end. Steps 1-5 deal with data manipulation. We train our model in step 6 and that’s where MLflow comes into picture. 1 2 3 4 5 6 7 8 9 10 11 12 13 @pipeline def ml_pipeline(): # 1. fetch training data texts, target = get_training_dataset() # 2. minimal text preprocessing # 3. tfidf vectorization vectorizer, X = get_vectorizer_and_features(preprocess_text(texts)) # 4. target encoding target_encoder, encoded_targ...

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