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feat(mlflow): bump to 2.19.0
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README.md

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@@ -13,6 +13,7 @@ The package leverages several [tools](#tools) and [tips](#tips) to make your MLO
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You can use this package as part of your MLOps toolkit or platform (e.g., Model Registry, Experiment Tracking, Realtime Inference, ...).
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**Related Resources**:
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- **[LLMOps Coding Package (Example)](https://github.com/callmesora/llmops-python-package/)**: Example with best practices and tools to support your LLMOps projects.
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- **[MLOps Coding Course (Learning)](https://github.com/MLOps-Courses/mlops-coding-course)**: Learn how to create, develop, and maintain a state-of-the-art MLOps code base.
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- **[Cookiecutter MLOps Package (Template)](https://github.com/fmind/cookiecutter-mlops-package)**: Start building and deploying Python packages and Docker images for MLOps tasks.
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docker-compose.yml

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services:
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mlflow:
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image: ghcr.io/mlflow/mlflow:v2.18.0
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image: ghcr.io/mlflow/mlflow:v2.19.0
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ports:
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- 5000:5000
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environment:

notebooks/explain.ipynb

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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Features: ['categoricals__season_1', 'categoricals__season_2', 'categoricals__season_3', 'categoricals__season_4', 'categoricals__weathersit_1', 'categoricals__weathersit_2', 'categoricals__weathersit_3', 'categoricals__weathersit_4', 'numericals__yr', 'numericals__mnth', 'numericals__hr', 'numericals__holiday', 'numericals__weekday', 'numericals__workingday', 'numericals__temp', 'numericals__atemp', 'numericals__hum', 'numericals__windspeed', 'numericals__casual']\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"shap_values = shap.Explanation(\n",
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" samples_explanations, feature_names=samples_explanations.columns.to_list()\n",
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")\n",
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"shap_values.feature_names\n",
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"print(\"Features:\", shap_values.feature_names)\n",
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"shap_values"
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]
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},

notebooks/indicators.ipynb

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notebooks/processing.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [

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