Fundamentos de MLOps y tracking de modelos
驴Qu茅 es MLOps y para qu茅 sirve?
Tracking de modelos en localhost con MLflow
Tracking de modelos en localhost: directorio personalizado
Etapas del ciclo de MLOps
Componentes de MLOps
Tracking de modelos con MLflow y SQLite
Tracking de modelos con MLflow en la nube
Tracking del ciclo de vida de modelos de machine learning
Tracking de experimentos con MLflow: preprocesamiento de datos
Tracking de experimentos con MLflow: definici贸n de funciones
Tracking de experimentos con MLflow: tracking de m茅tricas e hiperpar谩metros
Tracking de experimentos con MLflow: reporte de clasificaci贸n
Entrenamiento de modelos baseline y an谩lisis en UI de MLflow
MLflow Model Registry: registro y uso de modelos
Registro de modelos con mlflow.client
Testing de modelo desde MLflow con datos de prueba
驴Para qu茅 sirve el tracking de modelos en MLOps?
Orquestaci贸n de pipelines de machine learning
Tasks con Prefect
Flows con Prefect
Flow de modelo de clasificaci贸n de tickets: procesamiento de datos y features
Flow de modelo de clasificaci贸n de tickets: integraci贸n de las tasks
Flow de modelo de clasificaci贸n de tickets: ejecuci贸n de tasks
驴C贸mo se integra la orquestaci贸n en MLOps?
Despliegue de modelo de machine learning
Despligue con Docker y FastAPI: configuraci贸n y requerimientos
Despligue con Docker y FastAPI: definici贸n de clases y entry point
Despligue con Docker y FastAPI: procesamiento de predicciones en main app
Despligue con Docker y FastAPI: configuraci贸n de la base de datos
Despliegue y pruebas de modelo de machine learning en localhost
Despliegue y pruebas de modelo de machine learning en la nube
驴Qu茅 hacer con el modelo desplegado?
Monitoreo de modelo de machine learning en producci贸n
驴C贸mo monitorear modelos de machine learning en producci贸n?
Entrenamiento de modelo baseline
Preparar datos para crear reporte con Evidently
An谩lisis de la calidad de los datos con Evidently
Creaci贸n de reportes con Grafana
驴C贸mo mejorar tus procesos de MLOps?
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How to improve a classification model with Support Vector Classifier?
The development and improvement of classification models are essential in the field of machine learning. In this context, a second approach using Support Vector Classifier (SVC) has been proposed to improve the simplicity of the initial base model. The importance of this approach lies in its ability to handle multiclass classification and improve the accuracy of the model.
What are the key steps in model creation?
To implement an efficient SVC model, several carefully structured steps were carried out:
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Data transformation: Data transformation techniques such as inverse frequency were used to prepare the data for the model.
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Data partitioning: Data were separated into training and test sets to ensure that the model can generalize to new data.
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Model definition: A Support Vector Machine Classifier was configured by specifying attributes such as kernel and class weight, as well as setting a seed to ensure model reproducibility.
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Model Tuning: The model was trained with the transformed training data and evaluated using a test data set, and the predictions were examined for necessary adjustments.
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Model evaluation: A report was generated with the test data to evaluate the model's performance on the predicted classes.
How are sparse matrices processed?
Working with sparse matrices is common when using techniques such as frequency inverse, and can involve additional complexities. In this model:
- The rows of the matrix represented individual records, while the columns corresponded to single words.
- To simplify the analysis and avoid the increase in dimensions that such sparse matrices can cause, an alternative technique was employed that involved summing components in a weighted manner.
- This was done to condense the matrix information into a single scalar value, thus improving data handling and analysis.
How are predictions and references stored and used?
It is crucial to consider how predictions and reference data are managed when building an effective model:
- Predictions: Predictions were stored and added to the training set, allowing comparisons with actual labels and adjustments to the model.
- Data references: Reference data, or benchmark data, was linked to the test set for use in data quality studies and data variance analysis.
Why is data preparation important for Evidently?
The use of tools such as Evidently for data quality requires a specific format to ensure proper analysis:
- Pre-transformations: label values and predictions were transformed to appropriate data types, such as integers.
- Simplified datasets: Only the necessary columns were selected in a format suitable for Evidently, avoiding complex data structures.
What next after data transformation?
With the data now transformed, you are ready to perform a data quality analysis, verifying the model's predictions against a reference dataset and real data. This step not only evaluates the effectiveness of the model, but also helps ensure its robustness over time.
Continue to explore the capabilities of SVC and experiment by creating your own baseline model. Don't forget to share your results and learn from the experiences of other students and look forward to the next lesson!
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