Internship← Data Science learning pathData Science · Module 9 of 10
Big data and deployment
Scale analyses and put models to work.
📖 Key ideas
- Large data: Spark, Dask, BigQuery, Snowflake.
- Save models with joblib; serve with FastAPI.
- Schedule pipelines with Airflow.
- Track experiments with MLflow.
- Monitor model performance and data drift.
💡 Real-life example: Deploying a model is moving a recipe from your home kitchen into a restaurant that serves thousands daily.
SparkDistributed data processing
joblibSaves Python objects/models
MLflowExperiment tracking
Data driftChanging input data