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Experiment Tracking Using MLflow in Machine Learning | Model Versioning & Model Registry | Part 1
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MLflow Live Demo | Experiment Tracking and Model Versioning
Topics Covered:
1. Train a Basic classifier using Random Forest
2. Create Experiment-Basic classifier
3. Log metrics, model, and other artifacts
4. Tune model using hyperparameter tuning using Randomized Search CV
5. Create another experiment in MLFlow- Optimised classifier
6. Use SQLite as the backend database for model registry
7. Explore MLflow UI, interpret experiments and runs
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LLM and Langchain live demo and code explanation:
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Topics Covered:
1. Train a Basic classifier using Random Forest
2. Create Experiment-Basic classifier
3. Log metrics, model, and other artifacts
4. Tune model using hyperparameter tuning using Randomized Search CV
5. Create another experiment in MLFlow- Optimised classifier
6. Use SQLite as the backend database for model registry
7. Explore MLflow UI, interpret experiments and runs
If you find this video helpful, don't forget to like share and subscribe.
LLM and Langchain live demo and code explanation:
Connect with me:
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