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End to End MLOps Pipeline on Optimizing Revenue through Dynamic Pricing using Databricks
6 Scenarios
6 Hours 45 Minutes
Intermediate
Industry
retail-and-cpg
Skills
data-understanding
data-wrangling
data-storage
ml-modelling
Tools
python
databricks
Learning Objectives
Demonstrating how to integrate and preprocess multiple datasets related to pricing, sales, and customer behavior for a regression problem.
Perform Exploratory Data Analysis (EDA) to identify key insights from pricing, competitor, and inventory data.
Apply feature engineering techniques to create meaningful predictors for revenue optimization.
Build and evaluate machine learning models to optimize pricing strategies.
Interpret model results and optimization outputs to provide actionable insights for business decision-making.
Implement MLFlow for Tracking Experiment and Model Artifacts
Deploying the Model for real-time Dynamic Pricing Inference
Overview
Prerequisites
- Building ML Solutions on Databricks
- Understanding of managing MLlifecycle using MLFlow
- Working knowledge of Inference Endpoints