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Industry
retail-and-cpg
Skills
data-understanding
approach
data-wrangling
data-visualization
data-quality
problem-understanding
ml-modelling
quality
model-deployment
code-versioning
git-version-control
programming
model-retraining
Tools
python
azure
mlflow
github
Learning Objectives
Develop skills in cleaning, aggregating, and normalizing historical sales, customer preference, and competitive data
Perform Exploratory Data Analysis (EDA) to identify key insights from sales, competitor, and inventory data.
Learn how to derive composite indicators, such as a channel’s overall performance score from raw metrics like units sold, customer preferences, and competitive pressure.
Build and evaluate machine learning models to determine optimal distribution channel.
Grasp the strategic importance of channel selection in launching a new beverage
Integrate MLflow for model tracking, versioning, and reproducibility.
Implement CI/CD pipelines for automating the deployment of machine learning models.
Deploy machine learning models to a production environment using industry-standard tools.
Develop testing strategies for validating machine learning models, including unit tests and model performance evaluations.
Overview
Prerequisites
- Building ML Solutions on Azure ML Studio
- Understanding of managing MLlifecycle using MLFlow
- Working knowledge of Inference Endpoints