Développement, industrialisation et MLOps d'un modèle de Machine Learning prédictif intégré dans Microsoft Fabric
Core
Develop, train, and operationalize predictive machine learning models within Microsoft Fabric, integrating feature engineering, model packaging, and automated scoring pipelines.
Role type
Machine Learning Engineer (MLOps)
Builds
Production-ready predictive models and automated scoring pipelines for business problems
Domain
Data Science / Machine Learning / Enterprise Software
Deliverable
production ML models
Required skills
Python, PySpark, Scikit-Learn, XGBoost, Pandas, MLflow, Data Factory, Power BI, Feature Engineering, Model Training, Model Evaluation, Pipeline Automation
Preferred skills
Microsoft Fabric, Synapse Data Science, Lakehouse architecture
Technologies
Microsoft Fabric, PySpark, MLflow, Data Factory, Power BI, Scikit-Learn, XGBoost, Pandas
Responsibilities
Explore, clean, and transform historical data in Lakehouse using PySpark notebooks; Develop, train, and evaluate multiple ML algorithms; Track training cycles, record performance metrics, and package the best model using MLflow; Automate scoring pipelines via Data Factory to feed the Gold layer; Design Power BI reports to visualize predictions and performance indicators.
Seniority
Mid-level (B+5 / Engineering School Graduate)