Implementing end-to-end DataOps and MLOps including but not limited to machine learning models, data analysis, data visualisation; generally speaking, problem solving in the mentioned areas.
The following tools and libraries can be used:
Python, Spark, MLflow, Docker, Jupyter, Databricks, TensorFlow (Keras), scikit-learn, pandas, NumPy, XGBoost, Git (GitHub, GitLab), GitHub Actions, Azure DevOps, pytest, SQL (MySQL), Azure, Matplotlib and Tableau